Artificial Intelligence (AI) in Surface Water Management: A Comprehensive Review of Methods, Applications, and Challenges

Abstract

Surface water systems face unprecedented stress due to climate variability, urbanization, land-use change, and growing water demand—prompting a shift from traditional hydrological modeling to intelligent, adaptive systems. This review critically explores the integration of Artificial Intelligence (AI) in surface flow management, encompassing applications in streamflow forecasting, sediment transport, flood prediction, water quality monitoring, and infrastructure operations such as dam and irrigation control. Drawing from over two decades of interdisciplinary literature, this study synthesizes recent advances in machine learning (ML), deep learning (DL), the Internet of Things (IoT), remote sensing, and hybrid AI–physics models. Unlike earlier reviews focusing on single aspects, this paper presents a systems-level perspective that links AI technologies to their operational, ethical, and governance dimensions. It highlights key AI techniques—including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Transformer models, and Reinforcement Learning—and discusses their strengths, limitations, and implementation challenges, particularly in data-scarce and climate-uncertain regions. Novel insights are provided on Explainable AI (XAI), algorithmic bias, cybersecurity risks, and institutional readiness, positioning this paper as a roadmap for equitable and resilient AI adoption. By combining methodological analysis, conceptual frameworks, and future directions, this review offers a comprehensive guide for researchers, engineers, and policy-makers navigating the next generation of intelligent surface flow management.

1. Introduction

Surface water systems—including rivers, lakes, and reservoirs—are essential components of the hydrological cycle and form the foundation of many ecological, agricultural, and urban systems worldwide [1]. They regulate streamflow, transport sediments and nutrients [2,3], support biodiversity, and provide critical resources for drinking water, energy, and irrigation [4,5]. However, the complexity of managing surface flows has grown significantly in recent decades due to climate-induced variability, rapid urbanization, altered land-use patterns, and increasing water demand [6,7]. These pressures have led to heightened flood risks, water scarcity, degraded water quality, and sediment accumulation in rivers and reservoirs, underscoring the urgent need for dynamic, predictive, and integrated management solutions [8,9].

Traditional surface flow management approaches have depended mainly on physically based and conceptual hydrological models such as the Soil and Water Assessment Tool (SWAT), Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) [10], and Water Evaluation and Planning System (WEAP), which simulate the behavior of water systems using governing equations of mass and energy balance. While these models are grounded in hydrological theory and are widely used in research and policy, they often require extensive parameterization, calibration, and computational resources [11]. Furthermore, many traditional hydrological models face difficulties adapting to nonstationary conditions introduced by climate and land-use changes, particularly when relying on static parameters or long-term calibration datasets [12]. While some models can be enhanced to account for such dynamics through data assimilation or real-time updating, doing so often requires significant effort and expert intervention [13]. Similarly, integrating high-frequency data from modern sensors and satellite platforms is possible but typically demands custom interfaces or post-processing, limiting operational flexibility and real-time responsiveness in practice [14,15]. These limitations restrict their utility in real-time applications and reduce their predictive robustness in uncertain or poorly instrumented environments [8].

Artificial Intelligence has emerged as a transformative solution to address these shortcomings [16]. AI encompasses a suite of computational techniques—including ML, DL, fuzzy logic systems, and hybrid AI–physics models—that can autonomously analyze large datasets, detect complex nonlinear relationships, and make data-driven decisions [17,18,19]. In hydrology, AI techniques have been applied to streamflow prediction, flood forecasting, irrigation scheduling, urban drainage control, and infrastructure monitoring, such as Long Short-Term Memory (LSTM), Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) [20,21,22], Gated Recurrent Units (GRUs) [23], and Transformer-based sequence models [24,25,26]. These models are discussed in this paper, specifically how they outperform traditional models in accuracy and adaptability and enable near real-time forecasting when integrated with the IoT and remote sensing systems [27].

In addition to improving predictions, AI is transforming infrastructure operations across water sectors. The following sections provide a structured synthesis of AI applications across surface water domains, followed by comparative analysis, ethical considerations, and future directions. Dams and reservoirs benefit from AI through inflow prediction, rule curve optimization, and structural health monitoring using computer vision and XAI frameworks [28]. In agriculture, AI-powered irrigation systems leverage soil and climate data to automate water delivery and maximize efficiency using algorithms such as Support Vector Machines (SVMs) [29], Random Forest, and fuzzy logic [30,31]. Urban drainage systems are also being enhanced through AI-enabled models that simulate stormwater flows, predict urban flooding [24], and recommend adaptive control strategies [32,33]. These innovations demonstrate that AI is not merely a tool for modeling but a cornerstone for intelligent decision support across scales and sectors.

Beyond modeling and prediction, AI has also emerged as a powerful tool for decision-making in surface water management, offering alternatives to traditional frameworks such as Multi-Criteria Decision Analysis (MCDA), as tackled in the next sections. MCDA methods—including the AHP (Analytic Hierarchy Process) [34,35,36], Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [37], and Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) [38]—have long been employed to evaluate trade-offs among conflicting objectives such as flood control, water supply, cost efficiency, and ecological health [38,39]. While MCDA provides a transparent, stakeholder-driven framework, it often relies on static criteria weights and expert judgments, which may not reflect evolving environmental conditions or incorporate real-time data. In contrast, AI-driven decision support systems, such as Reinforcement Learning (RL) [40], fuzzy logic controllers [41], and evolutionary algorithms like Genetic Algorithms (GAs) [42] and Particle Swarm Optimization (PSO) [43]—offer dynamic, adaptive strategies that can optimize reservoir releases, irrigation schedules, and drainage operations in real-time [44,45]. These AI methods learn from historical and streaming data, allowing for continuous improvement and adaptive decision-making under uncertainty. Furthermore, hybrid AI-MCDA models are emerging that combine the interpretability of MCDA with the computational intelligence of AI, thereby enabling more robust, data-driven, and context-sensitive decision-making frameworks for water resource planning [36].

Despite these advances, the integration of AI in surface flow management still faces significant challenges. One primary concern is the limited transferability or generalization of AI models trained on region-specific datasets, which often perform poorly in different hydrological or climatic contexts [17]. While it is well-understood that AI models must be trained on specific datasets, this poses a significant obstacle in data-scarce regions or ungauged basins, where retraining on a local dataset may not be feasible. In these settings, generalization capacity becomes essential. Studies by Kratzert et al. (2019) [17], Nearing et al. (2021) [46], and Newman et al. (2017) [47] have shown that while models trained on large-sample datasets (e.g., Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) in the United States and Great Britain) can capture broader hydrological patterns, their performance significantly deteriorates when applied to hydrologically dissimilar or climatically distinct basins. This has led to the exploration of regionalization techniques [48], meta-learning frameworks [49], and transfer learning approaches [50], where pre-trained models are fine-tuned for a targeted basin using limited local data.

Furthermore, integrating hydrologic theory into machine learning architectures, such as in Physics-Informed Neural Networks (PINNs) or hybrid models, has improved generalizability while maintaining physical realism [11,18,51]. These emerging strategies aim to address the operational challenge of deploying robust AI models in regions with limited historical data, rapid land-use changes, or changing climate regimes, which are common in many developing countries. Another critical issue is the lack of interpretability in many deep learning models, which impedes their acceptance by engineers and water managers, particularly in high-risk applications like flood forecasting or dam operations [14]. Moreover, the ethical dimensions of AI remain underdeveloped in water science. AI models can unintentionally reinforce data biases and inequities if marginalized communities or regions are underrepresented in training data [52]. Concerns about algorithmic transparency, accountability, and data privacy further complicate deployment, especially in public sector settings where governance and trust are paramount [53,54].

One of the most critical gaps identified in the current body of literature is the lack of integrated frameworks that connect AI’s technical capabilities with its operational, ethical, and institutional dimensions in surface flow management. While several studies have successfully demonstrated the application of AI in hydrological modeling, infrastructure control, or real-time monitoring, many remain confined to narrowly scoped case studies conducted in well-instrumented or academically curated environments [15,55]. These models often rely on clean, high-resolution data unavailable in most operational settings, particularly in developing regions. Consequently, their generalizability and robustness are limited when deployed in data-scarce or socio-environmentally complex contexts [11,56]. There is a notable absence of holistic approaches that assess how AI models perform across diverse socio-environmental contexts, especially in data-scarce or high-risk regions where institutional capacity is weak and adaptive governance is limited. Moreover, very few reviews offer practical guidelines for scaling AI applications from pilot research to real-world implementation, particularly in developing countries where challenges related to digital infrastructure, local expertise, and participatory engagement persist. This review addresses this pressing gap by emphasizing not only the technological advancements of AI but also the necessary conditions—such as explainability, fairness, and institutional readiness—that must accompany its widespread adoption in surface flow management systems.

While existing reviews have explored subsets of these applications—such as AI in flood modeling [57], Neural Networks for streamflow prediction [58], or AI for water quality assessment [59]—a comprehensive, multi-sectoral synthesis remains lacking. These prior works often focus narrowly on technical performance without fully addressing AI integration’s socio-technical, ethical, and governance dimensions. In contrast, this review examines AI’s role across hydrological modeling, water infrastructure operation, and real-time environmental monitoring, with equal attention to hybrid modeling frameworks, challenges of explainability and generalization, and AI deployment’s ethical and institutional implications.

This paper aims to address this critical gap by synthesizing recent advances in AI-driven surface flow management and identifying key research opportunities and implementation challenges. This review compares traditional hydrological modeling approaches with emerging AI-based methods, followed by a structured exploration of AI applications in streamflow forecasting, dam operations, smart irrigation, urban drainage, and real-time monitoring. It further examines the development of hybrid modeling frameworks that integrate AI with physically based models and critically analyzes cross-cutting concerns such as algorithmic bias, model interpretability, institutional readiness, and interdisciplinary collaboration. Ultimately, this review consolidates the current knowledge and proposes a strategic roadmap to guide future research, enhance operational practice, and support equitable, transparent, and adaptive water governance in an era increasingly shaped by intelligent systems.

2. Methods

This review employs a structured literature synthesis to explore the integration of AI in surface flow management (Figure 1). Systematic and narrative review techniques were applied to identify, evaluate, and synthesize relevant research studies, tools, and conceptual frameworks from interdisciplinary fields, including hydrology, computer science, environmental engineering, and decision science.

2.1. Literature Search and Data Sources

Peer-reviewed articles, review papers, and relevant reports published between 2000 and 2025 were gathered from Scopus, Web of Science (WoS), ScienceDirect, IEEE Xplore, and Google Scholar databases. Keywords used in the search included the following:

• “Artificial Intelligence in hydrology”;
• “AI-based flood prediction”;
• “Machine Learning in water infrastructure”;
• “Real-time water monitoring using AI”;
• “AI for dam operation”;
• “Hybrid hydrological modeling”;
• “Explainable AI in water systems”;
• “Ethics in AI water management”.

The inclusion criteria for this review required that studies explicitly applied or discussed AI techniques within the context of surface water management, covering domains such as streamflow prediction, sediment transport, flood forecasting, water quality monitoring, dam operations, or irrigation systems. Only publications written in English and containing clear methodological insights or empirical findings were considered eligible. To maintain academic rigor and ensure the reliability of findings, gray literature, theses, and non-peer-reviewed sources were excluded from the analysis.

2.2. Review Structure and Thematic Analysis

Selected articles were systematically categorized according to their primary application domains: AI in Hydrology, AI in Infrastructure Operations (including dams, irrigation, and drainage systems), Real-Time Monitoring and Sensor Integration, Hybrid AI and Physics-Based Modeling, and Decision-Making and Ethics in AI Deployment. A thematic coding approach was employed to extract detailed insights about AI methodologies and compare their performance across applications. Implementation challenges, including data limitations and model interpretability, were also documented. Visual figures and conceptual diagrams were developed to illustrate system-level interactions and technological linkages to enhance clarity and support comparative analysis. In total, 127 peer-reviewed articles were included in this review, identified through a structured screening process involving five major academic databases and supplementary sources. These studies were thematically classified into eight key application domains: streamflow forecasting, flood prediction, water quality monitoring, dam operations, smart irrigation, urban drainage, hybrid modeling, and ethics and decision-making frameworks. As shown in Figure 1, streamflow forecasting and flood prediction emerged as the most frequently studied areas.

To facilitate systematic cross-domain analysis, each article was tagged using a thematic coding scheme capturing the AI method employed (e.g., ML, DL, hybrid, or evolutionary algorithms), the type of data used (e.g., remote sensing, IoT, historical records), performance metrics (e.g., RMSE, NSE, R²), and recurring implementation challenges such as data sparsity, limited generalizability, or lack of interpretability. This structured approach enabled the identification of domain-specific performance patterns, common technical barriers, and innovation gaps in the deployment of AI for surface flow management. Each study was critically assessed for its methodological clarity, scope, model validation, and replicability. Comparative discussions were drawn against existing reviews to highlight the novelty of this work, particularly its integration of ethics, decision-making frameworks, and cross-sectoral perspectives not comprehensively addressed in previous studies.

3. Traditional vs. AI Approach

Water resources management has traditionally relied on physically based and empirical models, which simulate hydrological processes based on established physical laws and are widely used for infrastructure planning. However, these models often require extensive calibration and are less adaptable to rapidly changing or poorly monitored environments [11,15]. AI-based approaches offer data-driven flexibility but are similarly constrained when datasets are short, sparse, or contain significant gaps. In such cases, AI models may overfit, yield physically implausible outputs, or fail to generalize beyond the training domain [17,60]. This is particularly problematic in ungauged basins or developing regions with limited hydrological monitoring [14,55]. To overcome these limitations, recent advances emphasize hybrid models—such as Physics-Informed Neural Networks—that integrate domain knowledge into AI architectures, improving both interpretability and robustness [18,51]. While grounded in well-established scientific principles, these conventional methods often suffer from several limitations; they require substantial calibration, are computationally expensive, and frequently struggle to adapt to nonstationary conditions such as those introduced by climate change, land use alteration, or population growth [11,15]. In contrast, AI offers a dynamic and adaptive alternative by learning complex patterns directly from data, enabling improved real-time responsiveness, predictive accuracy, and system optimization [61,62].

Table 1. This table highlights key differences between conventional water management methods and AI-driven approaches, illustrating how Artificial Intelligence enhances efficiency, decision-making, data integration, and monitoring capabilities in hydrological systems.

Traditional Approach	AI Approach	References
Manual data acquisition and data analysis	Real-time data acquisition (IoT) and efficient data analysis	[61]
May lack inclusive decision-making	Intelligent decision-making	[62]
Rely on historical data for hydrological modeling	Combines historical data, AI, remote sensing, and cloud computing for flood modeling	[18]
Involves laborious, expensive, and time-consuming laboratory methods	Can handle massive amounts of data and uses remote sensing and the IoT for quality monitoring	[27,63]
Lack of comprehensive data monitoring	Heavily depends on data and needs expertise	[11]

presents a comparative overview of traditional and AI-based surface water management approaches, emphasizing how AI improves operational efficiency, real-time decision-making, comprehensive data utilization, and monitoring effectiveness in hydrological applications.

Traditional approaches typically rely on manual data acquisition and offline analysis, making responding quickly to environmental changes difficult. They often lack real-time decision-making and require extensive expert intervention, especially in areas like hydrological modeling, flood prediction, or water quality monitoring [61]. For example, laboratory-based water quality assessments remain labor-intensive, expensive, and time-consuming [63]. In contrast, when supported by adequate sensor infrastructure and robust data streams, AI models integrated with the IoT and remote sensing enable real-time, automated analysis, drastically reducing the delay between data collection and actionable decision-making [19,27]. However, this advantage depends on data quality and availability; in cases where datasets are short, noisy, or spatially inconsistent, AI models may yield unreliable predictions or fail to generalize effectively, similar to the limitations observed in traditional models [17]. Traditional hydrological models typically use fixed equations and calibrated parameters, which can limit their adaptability under changing climatic or land use conditions. While AI models offer greater flexibility by learning directly from data, they are equally vulnerable to poor generalization when datasets are short, inhomogeneous, or unrepresentative of future scenarios [60,64].

Both approaches, therefore, face challenges in nonstationary environments, underscoring the importance of hybrid modeling frameworks that combine traditional models’ physical realism with AI’s adaptability to enhance robustness and transferability [14,18,21,65]. This presents a critical limitation when dealing with extreme events or rapidly evolving environmental conditions. AI overcomes this by offering data-driven, adaptive modeling that improves with more data and can uncover nonlinear patterns that physics-based models may miss [17,18]. For instance, Zou et al. (2023) demonstrated how AI-based flood forecasting using deep learning significantly outperformed conventional rainfall runoff models’ accuracy and lead time [62].

Conventional surface water quality management methods depend on periodic grab sampling and chemical lab testing. These methods lack spatial and temporal granularity and are challenging to scale for continuous watershed monitoring. With remote sensing and real-time IoT networks, AI enables continuous water quality assessment by analyzing parameters such as turbidity, pH, and biochemical oxygen demand (BOD) [27,63]. These AI-enhanced systems can detect anomalies autonomously, significantly reducing manual sampling efforts and operational costs. Moreover, they strengthen early warning capabilities for pollution events, contributing to more responsive environmental management and public health protection [19,59].

However, AI has limitations, many of which parallel the challenges seen in traditional hydrological models. Most AI techniques rely on large volumes of labeled, high-quality data to function reliably. In data-scarce or poorly instrumented regions, insufficient training data can lead to poor generalization, model overfitting, and degraded predictive skills in AI models, often resulting in biased outputs or operational failure when applied beyond the training context [15,17,46]. Furthermore, AI models—particularly deep learning architectures—usually operate as “black boxes,” making it challenging to interpret predictions in mission-critical applications like flood forecasting or dam operation [11,15]. This lack of transparency can hinder trust among practitioners and regulators. Additionally, implementing AI solutions requires advanced machine learning and data science expertise, which remains a barrier in many water institutions, especially in low-resource settings [51]. These challenges reinforce the need for hybrid approaches that blend AI with physical knowledge and emphasize explainability, data realism, and institutional capacity.

Nonetheless, hybrid models combining AI with traditional hydrological modeling are helping overcome these limitations. These advancements underscore AI’s growing role in transforming traditional monitoring frameworks into more adaptive, efficient, and predictive systems.

Hybrid models integrating AI with physics-based frameworks are increasingly gaining attention in hydrology and water resources management. While traditional physically based models have long been the cornerstone for understanding surface flow dynamics, their limitations in handling nonlinearities, sparse data, and computational complexity have become more evident in recent years. Conversely, purely data-driven AI models—though excellent in learning patterns—often lack physical consistency and generalizability beyond their training domain [11,15]. The solution lies in hybrid modeling, which combines the scientific fidelity of physics-based approaches with AI’s adaptability and learning capacity to produce accurate, efficient, and interpretable outcomes for various water-related applications.

Streamflow forecasting is one of the most compelling uses of hybrid models in hydrology. Duan et al. (2023) [18] proposed a hybrid model integrating deep learning with the NWM to improve daily streamflow prediction. This hybrid framework embeds hydrological process knowledge into the Neural Network architecture, improving performance, particularly in ungauged or data-scarce regions. Similarly, Farfán et al. (2020) [21,66] combined GR2M and WEAP with ANNs, enhancing flow simulations in Andean river basins where glacial melt contributes to runoff. These hybrid models significantly outperformed their standalone counterparts by correcting bias and improving low-flow and peak-flow prediction reliability.

In flood forecasting, hybrid models are now widely used to account for spatiotemporal complexity and to reduce computational costs associated with physically based models. Another study demonstrated that physically constrained LSTM models produced better multi-step flood predictions than traditional rainfall runoff methods [17,67]. These models used physically relevant inputs such as soil moisture and rainfall intensity, thus maintaining model realism. Moreover, Hosseiny et al. (2020) [68] combined hydraulic simulation outputs with machine learning models like RF and Multilayer Perceptron (MLP) to improve flood extent and depth predictions, especially in urban drainage systems where data variability and system complexity are high.

Hybridization is also essential in sediment transport modeling, where dynamics are nonlinear and strongly affected by flow energy and catchment characteristics. Nourani and Kalantari (2012) [9] demonstrated using a Wavelet–ANN hybrid model that outperformed conventional regression models in capturing peak sediment discharge and short-term variability. The model’s capacity to process multiscale features of sediment transport dynamics made it particularly valuable for reservoir sedimentation forecasting and soil erosion planning.

Another central area of application is model error correction. For instance, Mohanty et al. (2024) [65] presented a hybrid Physically based Soil and Water Assessment Tool–Wavelet-enhanced Bidirectional Long Short-Term Memory (PSWAT-WBiLSTM) model. SWAT outputs were used as inputs to a Bidirectional LSTM that corrected systematic errors in runoff simulation. This coupling enhanced forecast accuracy, particularly in paddy-dominated catchments with complex irrigation, soil moisture feedback, and rainfall interaction.

In addition, hybrid optimization models are gaining traction for flood risk mapping and operational planning. Al-Areeq et al. (2024) [69] used a hybrid model based on the Levenberg–Marquardt (LM) algorithm and the Gbest-guided Artificial Bee Colony with Directed Scout (GABCds) to generate accurate flood susceptibility maps in arid environments. This approach demonstrated improved spatial resolution and statistical robustness compared to traditional susceptibility models, proving useful in regions with limited ground-based data.

The most recent innovations involve Physics-Informed Neural Networks (PINNs), where governing physical equations (e.g., mass and energy conservation laws) are embedded directly into the structure of AI models. These models improve generalization and help extrapolate to unseen conditions, such as future climate scenarios. Karpatne et al. (2017) [14] and Reichstein et al. (2019) [11] noted that PINNs and theory-guided AI approaches reduce data dependency and ensure scientific consistency, which is crucial for stakeholder trust and operational deployment in water systems.

Despite the benefits, challenges in hybrid modeling remain. These include the increased complexity of model integration, computational overhead, the need for interdisciplinary collaboration, and issues with model explainability. Proper preprocessing, sensitivity analysis, and cross-validation are critical to avoiding overfitting and ensuring robust outputs [15,60].

In both traditional and AI-based decision support systems for surface water management, the evolution in the number and complexity of decision criteria has been notable. Traditional MCDA methods such as AHP [35,70], TOPSIS [71], and PROMETHEE often rely on a relatively limited set of predefined criteria constrained by expert judgment and static weight assignment [38,72]. Historically, these criteria focused on technical, economic, and hydrological indicators such as cost, flood risk, water supply reliability, or infrastructure efficiency. However, as sustainability, climate resilience, environmental equity, and socio-political dimensions have gained importance, the number of relevant decision criteria has grown significantly. New considerations now include climate adaptability, carbon footprint, biodiversity impact, stakeholder inclusion, real-time data responsiveness, and ethical governance.

This expansion has challenged the scalability of traditional MCDA methods, which are often limited by cognitive load and the static nature of weight assignments. In contrast, AI-based decision support tools—such as Reinforcement Learning, fuzzy logic controllers, and evolutionary algorithms—can dynamically handle large, multi-dimensional criteria sets, drawing from high-frequency sensor data, satellite imagery, and participatory inputs [44,45]. These systems can learn relationships between criteria in complex and uncertain environments and adjust real-time decisions. Furthermore, hybrid AI-MCDA models now combine the interpretability of traditional MCDA with the adaptive, high-dimensional analysis capabilities of AI. This evolution enables a shift from fixed, top–down criteria structures toward context-sensitive, data-driven, and stakeholder-responsive frameworks for water decision-making [36].

Overall, the number and diversity of decision criteria in water management are expected to continue increasing in response to climate change, social equity demands, and the push for integrated, multi-benefit infrastructure solutions. AI-supported systems are particularly well-positioned to manage this complexity, provided issues of interpretability, fairness, and transparency are addressed.

4. AI Integration in Surface Flow Management

This section explores how AI tools are being applied across various hydrological domains, including modeling, infrastructure control, and monitoring, and how they reshape operational practices toward more resilient and sustainable water systems. Figure 2 shows the conceptual overview of how AI is utilized in surface water management.

4.1. Overview of AI Models in Surface Flow Management

Artificial Intelligence (AI) has become a cornerstone in modern surface water modeling, offering advanced capabilities to analyze complex hydrological systems. AI models used in this domain can be broadly categorized into machine learning, deep learning (DL), evolutionary algorithms, and emerging hybrid or physics-informed approaches.

Machine learning algorithms—such as Support Vector Machines, Random Forests, and Gradient Boosting—have been widely applied in rainfall runoff modeling, drought classification, and water quality assessment. These models excel in learning input–output relationships from structured datasets [57]. However, their reliance on manually selected features and limited memory of past states can constrain performance in dynamic systems.

Deep learning architectures, including ANN, LSTM, GRU, and CNN, are particularly effective for modeling nonlinear time series and spatial phenomena. They have demonstrated state-of-the-art streamflow forecasting and flood prediction performance by capturing long-term dependencies and complex hydrological interactions [15,17]. Recent advances include hybrid CNN-LSTM and Transformer models for spatiotemporal forecasting [73,74].

Evolutionary algorithms, such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO), are frequently used for multi-objective optimization tasks, including reservoir rule curve design, irrigation scheduling, and urban drainage control. These algorithms explore large decision spaces efficiently, particularly in complex systems with conflicting objectives [75,76].

Hybrid and physics-informed models represent a rapidly growing area. Physics-Informed Neural Networks (PINNs) embed hydrological laws directly into deep learning architectures, enabling AI models to respect physical constraints while learning from data [11,17]. Meanwhile, hybrid AI-MCDA approaches integrate AI with traditional decision support tools like AHP and TOPSIS, offering improved adaptability, explainability, and stakeholder relevance in water resource planning [34,77].

This review categorizes and critiques the application of these model types across key water domains—including streamflow, flood, water quality, dam operation, and decision-making—highlighting their technical strengths, contextual limitations, and future research needs.

4.2. AI Applications in Hydrology

Streamflow prediction has been one of the most thoroughly explored domains for AI integration in hydrology. Unlike conventional models relying heavily on historical calibration and simplified assumptions, AI models learn complex, nonlinear relationships from data directly. Long Short-Term Memory networks, a type of Recurrent Neural Network (RNN), have shown outstanding performance in modeling temporal dependencies within streamflow series, especially in data-scarce or ungauged basins [17,26,78]. For instance, Zhang et al. (2024) implemented a hybrid STA-GRU (Spatio-Temporal Attention Gated Recurrent Unit) model for flood forecasting, which achieved significantly lower prediction error and enhanced generalization by integrating real-time monitoring networks [24]. Similarly, Ghimire et al. (2021) employed CNN-LSTM architectures to forecast daily flows in Australian catchments, outperforming Decision Tree (DT), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost) algorithms in terms of root mean square error (RMSE) and correlation metrics [22].

Another vital area is flood forecasting, where rapid prediction and early warning are critical for saving lives and reducing economic losses. In contrast, AI models like Deep Convolutional Generative Adversarial Networks (DCGANs) and LSTM-based frameworks have demonstrated improved accuracy and faster runtimes. Cheng et al. (2020) showed that DCGANs effectively simulate the propagation of flood waves in both time and space, offering a scalable and robust approach to real-time urban flood prediction [79]. Complementing this, Tursi et al. (2023) applied an LSTM model to forecast flood peaks and timing in a medium-sized Mediterranean catchment in Southern Italy, with results indicating higher reliability than physically based models, especially under climate-induced uncertainty [78].

In hydrological modeling under climate variability, hybrid AI–physical models have provided new avenues to enhance prediction in ungauged or poorly instrumented basins. Guo et al. (2023) combined deep learning with the National Water Model (NWM), improving long-range streamflow forecasting by integrating physics-informed priors and real-time observations [8]. This increased predictive accuracy in complex hydrological systems and addressed the growing need for adaptable models for future climate scenarios.

AI has also proven effective in sediment transport modeling, where the interplay of flow energy, terrain, and land use creates highly nonlinear conditions. Nourani (2009) demonstrated that Artificial Neural Networks (ANNs) outperformed traditional sediment rating curves in predicting suspended sediment concentration at river mouths [80]. Expanding on this, Nourani and Kalantari (2012) incorporated wavelet transforms with ANNs to better capture the multiscale variability in sediment dynamics, leading to improved forecasting during storm-driven events [9]. These advances provide crucial support for designing sediment management strategies in reservoirs and deltas, which are highly sensitive to deposition processes.

Runoff modeling and watershed simulation have equally benefited from AI integration. Hybrid modeling approaches, such as the PSWAT-WBiLSTM framework proposed by Mohanty et al. (2024), have shown substantial improvements in simulating streamflow in agricultural basins. By combining the SWAT with deep learning-based sequence modeling, the framework effectively addressed uncertainties in rainfall runoff transformation and improved forecast skills [65]. Similarly, Farfán et al. (2020) showed that coupling WEAP and GR2M models with ANNs enhanced the prediction of low-flow events in Andean catchments impacted by glacier retreat [66].

Emerging techniques are further advancing the field of hydrology. Graph Neural Networks, capable of capturing spatial dependencies across connected river networks, have shown promise in distributed rainfall runoff modeling. Nelemans et al. (2025) demonstrated that GNN-based models outperform grid-based and lumped models in capturing spatiotemporal flow dynamics across European catchments [26]. Moreover, Transformer-based architectures, initially developed for natural language processing, have been successfully adapted for hydrological sequence forecasting. Perera et al. (2024) and Reza et al. (2022) applied a multi-head attention Transformer model that outperformed LSTM and GRU models regarding short- and long-term flow prediction across diverse climatic zones [25,81].

While the previous sections presented specific use cases of AI models in surface flow management, it is essential to synthesize and compare their characteristics to understand their strengths, limitations, and suitability across hydrological contexts. This section presents a deeper comparative analysis of ML, DL, evolutionary algorithms, and hybrid or physics-informed models.

Machine learning models such as SVM, RF, and GBM are widely used in water quality prediction, flood hazard mapping, and drought classification due to their relatively low data demands, robustness to overfitting, and ease of interpretation [57,82]. However, their performance declines when dealing with long-range temporal dependencies or complex spatial correlations typical in rainfall runoff modeling [15,74].

Deep learning models such as ANN, LSTM, GRU, and CNN perform superiorly in capturing nonlinear and dynamic relationships in time-series hydrology [17,83]. These models are particularly effective in streamflow prediction, flood forecasting, and rainfall estimation due to their ability to learn from large, high-dimensional datasets [15]. However, they are highly data-dependent and often act as black boxes, limiting their interpretability and acceptability in high-stakes water infrastructure management [46,74].

Hybrid and physics-informed models, including PINNs, process-guided deep learning, and AI-MCDA frameworks, combine domain knowledge with data-driven learning to improve model generalization and physical consistency. These models have shown promise in enhancing robustness in data-scarce or ungauged basins, reducing overfitting, and ensuring physical plausibility [17]. AI-MCDA integrations—such as those combining Neural Networks with AHP or TOPSIS—have improved stakeholder engagement and multi-criteria evaluation in infrastructure planning [45].

In response to the interpretability challenge of black-box AI models, XAI techniques have been applied to hydrology to enhance trust and transparency. Ryu and Lee (2025) developed a Dual-AI model that combines deep learning with explainable layers, allowing dam operators to visualize how various factors (e.g., rainfall, upstream inflow) contribute to inflow predictions [28].

Despite promising results, hybrid AI models in dam operations raise regulatory and safety concerns due to their complexity and lack of physical transparency. In critical infrastructure, decision-makers often require traceable outputs to ensure accountability and compliance with operational protocols [84,85].

These advancements support the broader integration of AI into operational decision-making and policy frameworks for water resources. Intelligence has become a transformative force in hydrology, enabling data-driven modeling of complex, nonlinear, and dynamic hydrological systems that were previously difficult to capture through traditional physical or empirical models. As global water challenges intensify due to climate change, urbanization, and land use shifts, AI offers unprecedented opportunities to enhance prediction accuracy, resilience planning, and sustainable water resource management.

4.3. AI in Water Infrastructure: Dams, Irrigation, and Drainage

Artificial Intelligence is increasingly recognized as a key enabler of intelligent, adaptive, and efficient water infrastructure systems. From large-scale dam operations to precision irrigation and flood-resilient drainage networks, AI facilitates real-time decision-making, predictive analytics, and optimization across multiple dimensions of water infrastructure [86,87,88]. These developments are driven by the convergence of AI with big data, the IoT, remote sensing, and cloud platforms [89,90].

Dams serve multiple critical functions, such as flood control, power generation, and water storage, but their operations are often hindered by inflow uncertainty, sedimentation, and structural stress. AI-based predictive models now play a pivotal role in enhancing dam inflow forecasting, structural health monitoring, and reservoir operations [28,91,92]. For instance, Ryu and Lee (2025) [28] developed a Dual-AI model combining deep learning and XAI to provide reliable, interpretable inflow forecasts essential for dam safety and reservoir rule curve updates. Similarly, computer vision (CV) techniques detect cracks and leakage in dam structures [93]. At the same time, optimization algorithms like Genetic Algorithms (GAs) and RL are being explored for multi-objective reservoir operation strategies [94,95].

Irrigation systems, particularly in agriculture-intensive or drought-prone regions, benefit significantly from AI-enhanced water scheduling, nutrient balancing, and evapotranspiration estimation [30,96]. AI enables precise irrigation through the analysis of sensor data (soil moisture, temperature, humidity) and weather forecasts using algorithms such as Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and ANN [97]. IoT-integrated systems like the one presented by Marol et al. (2025) [31], utilizing low-cost sensors and fuzzy logic, have succeeded in real-time automated irrigation while reducing water use by up to 30%. In addition, deep learning-based crop water requirement prediction tools enable seasonal irrigation forecasting, even in data-scarce areas [98]. The use of AI in irrigation scheduling must consider the socioeconomic context, including farmers’ access to technology, digital literacy, and trust in automated recommendations. Ethical concerns may also arise if models are biased toward specific crops or user groups [30,99].

Drainage and stormwater systems in urban areas face increasing pressure from impervious surfaces and extreme rainfall events. AI models—particularly those using ML classification and regression—have been utilized to predict flooding hotspots, optimize pipe sizing, and manage dynamic flow during storm events [32,100,101]. A notable example is the integration of ML with geospatial data in urban drainage planning, where terrain, land use, and rainfall intensity are used to forecast flooding risks and recommend proactive design changes [102,103]. These models are further enhanced by digital twin frameworks that simulate real-time drainage performance using synthetic and real-world datasets [104].

Within Water Distribution Networks (WDNs), AI has been instrumental in reducing energy consumption, enhancing pressure management, and minimizing leakage losses [105,106,107]. Genetic Algorithms, Particle Swarm Optimization (PSO), and Deep Reinforcement Learning (DRL) have been successfully applied to determine optimal pipe layouts, pump schedules, and valve placements under varying demand conditions [40,108,109]. Sangroula et al. (2022) [105], using the Standard Operating Procedure for Water Distribution Networks (SOP-WDN) algorithm with the Environmental Protection Agency Network Modeling Tool (EPANET), demonstrated that optimal design under hydraulic constraints led to lower operational costs and improved water quality. Meanwhile, Constraint Logic Programming (CLP) approaches have enabled efficient isolation valve placement, reducing system downtime during maintenance and repairs [106].

These innovations are particularly impactful when combined with remote sensing, Supervisory Control and Data Acquisition (SCADA) systems, and IoT-based platforms, enabling real-time visualization and management of entire water infrastructure networks [89,110]. Such AI-integrated frameworks enhance efficiency and ensure resilience against shocks such as droughts, floods, and infrastructure failure.

Additionally, evolutionary algorithms such as GA, PSO, and Ant Colony Optimization (ACO) have proven effective for multi-objective optimization, particularly in applications such as reservoir operation, irrigation scheduling, and stormwater control [45,84]. Their strength lies in exploring large decision spaces and handling nonlinear constraints. However, they are computationally expensive and often require coupling with simulation models to yield meaningful hydrological outputs applied in comprehensive analysis for infrastructure projects [76,82].

4.4. AI in Real-Time Monitoring

Real-time monitoring is critical in managing surface water systems, offering immediate insights into dynamic environmental processes. AI-driven real-time monitoring scheme is shown in Figure 3 as an example. Traditional water monitoring systems, which rely heavily on manual sampling and laboratory analysis, are often time-consuming, expensive, and unable to provide the temporal resolution needed to manage rapidly changing hydrological conditions. The emergence of AI, integrated with IoT devices and remote sensing platforms, has revolutionized the field by enabling autonomous, high-frequency monitoring across diverse water environments [18,30].

AI-powered real-time monitoring systems combine sensor technologies with machine learning algorithms to detect anomalies, forecast trends, and optimize responses to environmental stressors. In water quality monitoring, for example, sensors measuring turbidity, pH, dissolved oxygen, and conductivity are linked to AI models that interpret patterns and predict contamination events [19,27,86]. Frincu (2024) [27] demonstrated that deep learning and machine learning models such as SVM, RF, and ANNs could compute Water Quality Indices (WQIs) in real time, significantly improving early detection and classification of polluted sites. Akula et al. (2024) [19] further emphasized the utility of IoT-enabled AI platforms in wastewater monitoring, highlighting the ability to detect changes in pH and nutrient loads with high accuracy and minimal latency.

In addition, AI-enhanced streamflow monitoring integrates data from rain gauges, stream gauges, satellite observations, and wireless sensor networks to create comprehensive real-time profiles of river behavior [17,24]. Zhang et al. (2024) [24] developed a Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model that utilizes continuous monitoring station inputs to predict streamflow under diverse hydrological conditions accurately. These models enable real-time flood forecasting and improve water allocation and reservoir management during extreme events [17,22].

One of the notable innovations is the use of XAI to improve interpretability in real-time decision support. Ryu and Lee (2025) [28] incorporated XAI into a deep learning framework to monitor and predict dam inflows in real time. Their model allowed engineers to understand which variables (e.g., rainfall, inflow rate, reservoir level) most influenced the prediction, improving trust and operational decision-making.

Beyond hydrology, AI-IoT integration is also transforming urban drainage systems and smart irrigation networks. Selvam et al. (2020) [32] proposed an AI-based drainage system that leverages ML to monitor waste accumulation and optimize real-time drainage flow under varying rainfall intensities. Similarly, Vallejo-Gómez et al. (2023) [96] showed that fuzzy logic-based AI models, when combined with real-time data from soil moisture sensors and weather APIs, improve irrigation efficiency and reduce water loss by up to 30%. This demonstrates that AI is effective in modeling and instrumental in adaptive infrastructure control.

AI is particularly powerful when applied to multi-sensor data fusion, where heterogeneous datasets (e.g., satellite imagery, radar data, SCADA systems, and mobile sensor units) are synthesized to deliver real-time spatial intelligence. Recent advances include using Transformer architectures for time series modeling and GNNs for sensor network optimization [111]. These architectures allow systems to predict spatially distributed environmental parameters across unmonitored areas, enhancing resilience planning and response.

Despite these advancements, several technical challenges hinder the seamless operation of AI-driven systems in water management. Data latency—the delay between data capture and actionable output—can undermine real-time applications such as flood forecasting or drainage control [112]. Sensor reliability is another critical issue; low-cost IoT sensors often suffer from calibration drift, environmental noise, or data loss, particularly in remote or harsh environments [113]. Moreover, the increased connectivity of water infrastructure through the IoT raises cybersecurity risks, such as unauthorized access to control systems or data breaches affecting sensitive hydrological or operational data [92,93]. Recent studies, however, demonstrate that integrating AI with edge computing—which processes data near the source—and cloud-based dashboards can mitigate these issues. Edge AI reduces latency by enabling local decision-making and minimizes reliance on unstable network connectivity, while cloud platforms facilitate centralized monitoring, scalability, and secure multi-user access [19,51,88]. These solutions are increasingly being adopted in real-time monitoring frameworks for intelligent water networks and urban flood control systems.

4.5. Challenges and Ethical Considerations in AI-Driven Surface Flow Management

While Artificial Intelligence has revolutionized the way surface flow is modeled, monitored, and managed, its deployment is not without significant challenges. As AI systems become more deeply embedded in hydrological decision-making, new barriers emerge—ranging from technical limitations and data-related constraints to institutional inertia and ethical dilemmas (Figure 4). These challenges are particularly salient in water resource management, where decisions often carry life-saving implications, intersect with cultural and ecological values, and require coordinating complex socio-technical systems [11,15]. Moreover, the rapid acceleration of AI development has often outpaced the readiness of regulatory frameworks, organizational capacities, and public discourse, leading to a widening gap between innovation and implementation. This section critically examines the multidimensional challenges that must be addressed to ensure AI-driven systems in surface flow management are technically robust, equitable, explainable, and ethically aligned with the long-term goals of sustainable water governance.

4.5.1. Technical and Data-Related Challenges

AI models rely heavily on large volumes of high-quality data for training and validation. However, data availability, completeness, and consistency remain significant barriers, particularly in developing regions with sparse or degraded hydrological monitoring networks [17,114]. Even in data-rich areas, sensor malfunctions, missing values, and noise can introduce bias and reduce model reliability. In addition, the nonstationary nature of hydrological systems, influenced by climate change and land use dynamics, can limit the generalization of purely data-driven models [15,60].

These challenges, however, are not unique to AI. Traditional physically based models also suffer under nonstationary conditions and often require intensive calibration, which may not transfer well across time or space. One distinct limitation of many AI models—intense Neural Networks—is their lack of physical interpretability. While they may achieve high accuracy, their “black box” nature makes it challenging to understand the causal relationships between inputs and outputs, hindering their adoption in high-stakes settings like dam safety or flood early warning [11,14].

Recent advances encourage using physics-informed AI, explainable machine learning (XML), and hybrid modeling approaches incorporating domain knowledge into data-driven frameworks to address these parallel limitations. These strategies aim to preserve the flexibility of AI while enhancing trust, transparency, and physical realism in hydrological applications [17,64].

4.5.2. Institutional and Implementation Barriers and Data-Related Challenges

Adopting AI in water management requires cross-disciplinary collaboration between hydrologists, data scientists, engineers, and policy-makers. However, institutional structures in many water agencies are not yet equipped to support such integration [86,115]. A lack of technical expertise, resistance to change, and concerns over automation replacing human jobs are common hurdles in the public and utility sectors. Moreover, many AI models are developed in academic settings with limited pathways to operationalization. The technology-to-practice gap persists due to a lack of funding, unclear return on investment, and no standardized tools or protocols for validation, benchmarking, and integration with existing hydrological models [11,62].

Cybersecurity and data privacy concerns also surface with the proliferation of IoT- and cloud-based systems. Unauthorized access to AI-controlled infrastructure—such as automated dam gates or smart irrigation—can lead to catastrophic consequences. Yet cybersecurity strategies in water utilities often lag behind technological innovation.

4.5.3. Ethical and Societal Considerations

Ethical challenges extend beyond the technical domain. AI models inherently reflect the biases embedded in their training data. If specific communities, geographies, or data types are underrepresented, AI may amplify disparities in access to water services or disaster preparedness [116,117]. For instance, flood prediction models trained predominantly on urban data may fail to capture vulnerabilities in rural or informal settlements, potentially leading to unequal protection and delayed response during extreme events [118].

There is also growing concern around algorithmic opacity and accountability. When AI systems make critical decisions—such as allocating irrigation during drought or triggering flood evacuations—it is not always clear who is responsible when the system fails. Without proper governance, AI can undermine transparency and trust in public water institutions [53,119].

Furthermore, the risk of AI replacing human judgment raises philosophical and policy questions. While automation enhances efficiency, water resource management often involves values, trade-offs, and community priorities that require human deliberation. Decisions involving environmental justice, cultural values of water, or Indigenous water rights must not be fully delegated to algorithmic systems [53,120].

Ethical AI in water management must also consider intergenerational equity. Overreliance on AI that maximizes short-term optimization (e.g., for hydropower or irrigation) may neglect long-term sustainability goals such as ecosystem conservation and groundwater protection.

These ethical concerns are not merely theoretical. For example, AI-based flood forecasting systems trained predominantly on urban datasets have been shown to perform poorly in rural or informal settlements, exacerbating disparities in disaster response and resource allocation [118]. Similarly, opacity in model decision pathways raises accountability issues when AI is used for automated dam operations or irrigation scheduling—where failures could have life-threatening consequences [121,122]. Furthermore, in regions such as Australia and sub-Saharan Africa, the application of AI in water governance has raised criticism for overlooking Indigenous water rights and community participation, thereby reproducing long-standing environmental injustices [123]. To address these risks, AI systems must embed fairness into training datasets, clarify institutional responsibility, and ensure inclusive, transparent design processes that respect local knowledge and social values.

5. Discussion

Artificial Intelligence is no longer a peripheral tool but a transformative pillar in surface flow management, reshaping the foundations of hydrological modeling, infrastructure operation, and real-time monitoring. This review offers a comprehensive synthesis of AI’s applications across these domains. It stands out from prior works by systematically integrating technology typologies (e.g., machine learning, deep learning, hybrid models, the IoT) with practical hydrological contexts such as streamflow, sedimentation, flood control, water quality, and irrigation. Unlike earlier reviews that focused on isolated applications, such as Neural Networks in streamflow prediction [80], flood early warning systems [57], or machine learning for water quality monitoring [59]—This review establishes a systems-level perspective. It links hydrological complexity with institutional and ethical considerations, providing a more holistic understanding of AI’s transformative potential in water resource management.

Table 2. Summary of key references categorized by thematic focus. Each group illustrates the primary contribution of referenced studies to the evolving landscape of AI in surface water management, contextualized by domain-specific goals, methodological innovations, and known limitations.

Focus Area	Reference Numbers	Contribution Summary
Streamflow Prediction and Rainfall Runoff Modeling	[17,18,21,22,24,25,26,65]	Applied LSTM, STA-GRU, and CNN-LSTM models to improve streamflow forecasting in various climatic and topographic settings.
Flood Forecasting and Early Warning Systems	[57,62,67,68,69,78,79]	Developed deep learning models like DCGAN, LSTM, and Transformer for flood prediction with enhanced lead time and accuracy.
Water Quality Monitoring and Sediment Transport	[9,19,27,63,80]	Demonstrated AI applications in real-time water quality assessment and sediment concentration estimation using ANN and IoT data.
AI in Dam and Reservoir Operation	[28,91,92,93,94,95]	Used Explainable AI (XAI), computer vision, and optimization algorithms to enhance dam safety, inflow prediction, and rule curve optimization.
Smart Irrigation Systems	[30,31,96,97,98]	Showcased AI algorithms (SVM, KNN, fuzzy logic) for optimizing irrigation scheduling and improving water use efficiency.
Urban Drainage and Stormwater Management	[32,100,101,102,104,105]	Implemented ML-integrated digital twins and real-time models for flood mitigation, drainage optimization, and smart waste management.
Hybrid AI–Physics Models	[14,21,60,65]	Combined AI models with physical hydrologic models to improve realism and generalization in runoff and flood simulations.
Ethics, Bias, and Fairness in AI Deployment	[52,53,116,117,119,120]	Explored algorithmic bias, fairness, and data representation issues in AI for water systems; discussed transparency and accountability.
Institutional Readiness and Policy Integration	[86,87,88,89,90,110,115]	Identified implementation barriers such as lack of expertise, funding, and cybersecurity readiness in AI integration in water utilities.

summarizes the reviewed studies by clustering reference studies into key focus areas: streamflow forecasting, flood prediction, water quality monitoring, dam operations, smart irrigation, urban drainage, hybrid modeling, and ethical or institutional concerns. This thematic categorization highlights how various studies have advanced the application of AI in surface water management while also reflecting the diverse methodological approaches and evolving research directions. Importantly, while these contributions demonstrate the growing potential of AI, they also operate within the broader set of challenges outlined earlier, particularly regarding model generalization, data dependency, and transparency. These considerations—ranging from model explainability and data bias to regulatory limitations—are not confined to theory but influence each AI application domain differently. This review integrates such perspectives across forecasting, dam control, irrigation, and monitoring systems to provide a holistic understanding of real-world challenges.

A key trend is the shift from deterministic, physics-based hydrological models toward adaptive, data-driven frameworks. Extensive calibration requirements and poor performance under nonstationary climatic or land-use conditions often constrain conventional models such as SWAT, WEAP, and HEC-HMS [11,15]. In contrast, AI techniques—such as LSTM networks, STA-GRU, and Transformer-based architectures—demonstrate superior adaptability by learning complex, nonlinear patterns from multi-source datasets [17,26,78]. Hybrid frameworks that embed physical principles into AI, like PINNs, offer promising solutions for maintaining model transparency while boosting predictive performance [14,18].

This review contributes uniquely by analyzing AI’s cross-sectoral impact on water infrastructure. For example, dam operations leverage XAI for real-time inflow prediction and safety assurance [28], while computer vision techniques monitor dam health conditions [93]. Irrigation systems have been enhanced through AI-driven water scheduling based on sensor inputs and climate forecasts [30,97]. Urban drainage networks use ML-integrated digital twins to simulate dynamic stormwater flows [32,104], improving resilience against extreme rainfall. These applications depart from static, rule-based infrastructure toward intelligent, context-aware systems.

Real-time monitoring has shifted from research innovation to a fundamental requirement in modern water governance. AI-integrated IoT systems continuously assess water quality, streamflow, and rainfall anomalies, even in remote or poorly instrumented regions [19,27]. This capability is critical in low-income or data-scarce geographies, where immediate decision-making can prevent cascading disasters [111]. In these contexts, Edge-AI systems—which function independently of cloud infrastructure—offer robust solutions for decentralized water monitoring [88].

Nonetheless, significant challenges persist. Model transferability is a pressing issue, as AI models often struggle when applied outside their training regions due to differences in climate, terrain, and land use [17,33]. Explainability is another critical gap; black-box models are challenging to trust in high-stakes decision-making, particularly for flood evacuation or dam gate control tasks. However, techniques like Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) offer partial interpretability, and their integration into operational systems remains limited [28,124]. Ethical and fairness concerns are also underexplored; AI models trained on biased datasets may inadvertently exclude marginalized communities or misallocate water resources [117].

While this review highlights the growing role of AI in surface water management, it is essential to recognize that traditional physically based and conceptual models remain foundational tools in hydrology. Models such as SWAT, HEC-HMS, MIKE SHE, and MODFLOW have been extensively validated, are embedded in regulatory frameworks, and provide transparent, physically interpretable representations of hydrological processes. These models are particularly valuable in scenarios requiring expert judgment, long-term calibration records, and legal defensibility, such as infrastructure design or water allocation planning. Unlike many AI systems, they offer clear causal relationships and allow for scenario testing grounded in domain knowledge. Although AI models provide flexibility and automation, they face critical challenges in generalizability, explainability, and integration with existing water management protocols. Therefore, rather than framing AI as a replacement, this review advocates for hybrid or complementary approaches where AI enhances, rather than replaces, traditional methods—especially under nonstationary or data-sparse conditions.

One of the key takeaways of this review is that there is no universally superior AI model for surface flow management. The suitability of any given method—whether ML, DL, hybrid, or evolutionary—depends heavily on the specific characteristics of the watershed, the volume and quality of available data, the temporal resolution required, and the interpretability or operational needs of the decision-making process [15,46,74,125]. For instance, ML models offer simplicity and transparency but may underperform in capturing spatiotemporal complexity [15]. DL models provide high accuracy in complex, data-rich environments but often lack interpretability and require significant training data [74,125]. Hybrid models aim to bridge this gap by integrating physical knowledge with data-driven learning, making them promising but also computationally intensive [24,60,114]. Evolutionary algorithms are best suited for complex optimization tasks such as reservoir operation or rule curve design [50,116]. Therefore, model selection must be context-driven. This review does not prescribe a one-size-fits-all solution. Instead, it provides a comprehensive synthesis and comparative framework to support researchers and practitioners in selecting and adapting AI methods tailored to their application needs and local constraints.

From a governance perspective, the field still lacks institutional readiness. Many water utilities remain in manual processes and lack the technical capacity to deploy AI tools at scale. Cross-disciplinary partnerships and AI training programs for engineers and hydrologists are urgently needed. Moreover, policy, funding, and standardization gaps often block the transition from pilot models to operational systems [53]. The findings of this review hold important implications for water resource managers, decision-makers, and policy planners. First, integrating AI into surface flow management requires technical readiness and organizational adaptation, including the upskilling of staff, institutional trust-building, and updating operational protocols. Managers should be cautious when adopting AI tools and ensure that hybrid approaches are used where physical interpretability and legal defensibility are needed. Additionally, policy-makers should support the development of explainable, transparent AI models and provide funding and frameworks for participatory implementation, especially in data-scarce or high-risk regions. Ensuring institutional capacity and ethical governance mechanisms will be essential for sustainable and equitable AI adoption in water systems.

In addition to these limitations, this paper opens multiple new avenues for future research. Cooperative AI and multi-agent systems could revolutionize basin-wide water management by coordinating real-time dam releases, flood control, and irrigation [126]. The integration of digital twins at the river basin scale remains underdeveloped and presents a promising direction for real-time scenario analysis. Low-power AI chips for edge devices could make water monitoring accessible even in regions without Internet connectivity [88]. Incorporating downscaled climate projections into AI models would enhance long-term planning under climate uncertainty [11,18]. Finally, ethics-by-design frameworks—rooted in feminist, participatory, or Indigenous perspectives—can ensure that AI promotes equity, inclusion, and sustainability in water resource management [53,127].

This review makes a novel contribution by offering a multidimensional and ethically grounded analysis of AI applications in surface flow management. It synthesizes technical advancements and highlights institutional, ethical, and geographic disparities that must be addressed. This paper aims to catalyze the next generation of intelligent, resilient, and inclusive water systems through this integrative lens.

6. Conclusions

Artificial Intelligence (AI) has emerged as a transformative tool in surface flow management, with significant progress in applications such as streamflow forecasting, flood prediction, sediment transport modeling, water quality assessment, and intelligent infrastructure control. This review highlights that AI models—particularly deep learning architectures (e.g., LSTM, CNN), hybrid physics–AI approaches (e.g., PINNs), and decision-support tools (e.g., Reinforcement Learning, genetic algorithms)—can significantly enhance predictive accuracy and operational efficiency, especially when integrated with real-time sensing and remote data platforms. However, several objective outcomes have also been identified. First, AI-driven models offer substantial performance improvements in data-rich environments yet face challenges in generalization and robustness in data-scarce or ungauged basins. Second, hybrid and physics-informed models demonstrate greater physical consistency and transferability than purely black-box approaches. Third, significant gaps remain in model explainability, domain adaptation, and institutional readiness, especially in developing regions. Moreover, ethical considerations such as algorithmic bias, transparency, accountability, and digital inequality require urgent attention, as they pose barriers to the equitable deployment of AI systems in high-stakes water management contexts.

To address these limitations, future research should focus on the development of physically consistent and domain-adaptive models, integration of Explainable AI (XAI) frameworks to enhance interpretability and trust, and the promotion of participatory, co-designed tools that align technological innovations with stakeholder needs and institutional capacities. Bridging the gap between research and implementation will require interdisciplinary collaboration, policy alignment, and robust field validation to ensure that AI applications achieve technical success and support inclusive and climate-resilient water governance. Ultimately, the effective application of AI in surface flow management is case-sensitive and context-driven, as model performance is highly dependent on the characteristics of the watershed, the quality and resolution of available data, and the operational goals of the system being managed. In summary, while AI holds strong potential to modernize surface water systems, its success depends on context-aware, transparent, and ethically grounded approaches that advance performance and trust in real-world decision-making.