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AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition

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A Special Issue of Water (ISSN 2073-4441) belonging to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: 20 March 2027 | Viewed by 12024

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Dr. Bithin Datta

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Discipline of Civil Engineering, College of Science & Engineering, James Cook University, Townsville, Australia
Interests: hydrology; groundwater contamination; water resources; artificial intelligence
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Special Issue Information

Dear Colleagues,

The application and integration of Artificial Intelligence (AI), Machine Learning (ML) tools, and Digital Twin (DT) technologies are revolutionizing the wide and complex field of sustainable, long-term water resource management. These tools and technologies are enabling the development and application of efficient and reliable methodologies that are applicable to resource optimization, sustainability, infrastructure resilience, the management of natural disasters, the detection and remediation of contamination, the operation of reservoir systems, and the collection of remote data through smart sensors for optimal real-time resource management. The collection of remote data via UAVs is also able to address various real-world issues such as water table detection, contamination in surface water bodies, the management of aquatic habitats, the management of coastal aquifers and wetlands, efficient and water-sensitive irrigation planning, and the prediction and minimization of the impact of tsunamis on surface and subsurface water bodies through AI and ML. Modelling the quality of water on a regional scale without performing costly field measurements or predicting impending droughts and floods represents additional examples. The virtual representation of different components of the water resource system and their integration utilizing smart sensors and automated controls within a Digital Twin (DT) framework also represent advancements in the application of remote sensing, smart sensors, IOT, and feedback information. Ensembles of ML-based surrogate models, which are particularly useful in linked simulation and optimization-based decision models, are another rapidly growing area of application.

The development and utilization of innovative digital platforms that incorporate these tools and technologies for different spatial and temporal scales is rapidly gaining momentum. This Special Issue is dedicated to the field of water resource management, including the management of surface and subsurface water, and quantity and quality. It will also serve as a pivotal platform for the dissemination of cutting-edge research and practical applications.

This Special Issue will cover diverse topics related to water management by focusing on both theoretical advancements and real-world deployments that are relevant to digital platforms. This Special Issue will aim to bridge the gap between research and application.

This Special Issue encourages researchers, industry professionals, and policymakers to provide further insights into the challenges and opportunities of adopting these technologies. It will emphasize interdisciplinary collaboration, featuring contributions from hydrologists, data scientists, hydrogeologists, and engineers. Peer-reviewed articles, case studies, and reviews will ensure the dissemination of high-quality, impactful content. By fostering new ideas, dialogue, and innovation, this Special Issue aims to catalyze progress in the sustainable and intelligent management of water resources, aligning with global goals to address water security, resiliency, and sustainability.

Dr. Bithin Datta
Guest Editor

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

artificial intelligence (AI)
machine learning (ML)
digital twin
big data analytics
sensor networks
ensembles
decision models
smart sensors
digital platforms
surrogate models
water resource management
flood and drought forecasting
groundwater systems
surface water systems
contamination detection
linked simulation-optimization

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Related Special Issue

AI, Machine Learning and Digital Twin Applications in Water in Water (9 articles)

Published Papers (12 papers)

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Research

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23 pages, 5500 KB

Open AccessArticle

Machine Learning-Informed Hydrological Response Time Modeling in Tropical Watersheds

by Dagnenet Sultan, Nigussie Haregeweyn, Mitsuru Tsubo, Ayele Almaw Fenta, Tena Alamirew, Demesew A. Mhiret, Samuel Berihun Kassa, Ayele Mamo, Bewuketu Abebe Tesfaw and Atsushi Tsunekawa

Water 2026, 18(17), 2121; https://doi.org/10.3390/w18172121 - 28 Aug 2026

Viewed by 318

Abstract

Flood occurrence in tropical regions is intensifying due to climate variability and land-use change, increasing the need for reliable flood response time estimation. Accurate prediction of flood lag time (TL)—the interval between the centroid of excess rainfall and peak runoff—is critical for flood early warning and water resource planning. However, TL estimation remains challenging in data-scarce regions because of complex interactions among watershed morphology, rainfall characteristics, and runoff generation processes. This study evaluates four machine learning (ML) algorithms—Random Forest (RF), Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—for predicting TL across twenty gauged watersheds in the Blue Nile Basin of Ethiopia. Fourteen physiographic and hydro-climatic watershed characteristics were used as predictors. Among the tested models, XGBoost achieved the highest training performance (R² = 0.98, NSE = 0.96), while RF showed better generalization in the test dataset (R² = 0.77, NSE = 0.70, KGE = 0.71). SVM produced the lowest prediction errors (MAE = 0.95; RMSE = 2.25) but had lower explanatory power (R² = 0.49). To enhance interpretability and practical applicability, ML-based feature importance was used to develop a parsimonious empirical model: TL = 0.8 + 0.011A − 0.023RI, where A is watershed area and RI is rainfall intensity. This model explained 51% of TL variability and retained much of the predictive skill of more complex ML models. The proposed hybrid ML–empirical framework provides a transparent and operational approach for flood response time estimation in tropical highland watersheds. Its broader applicability remains subject to additional watershed-level validation and regional calibration. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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23 pages, 2360 KB

Open AccessArticle

A Machine Learning Approach to Hydrological Event Detection from News-Informed Social Media Alerts

by Joao Pita Costa, Gerald Corzo Perez, Oleksandra Topal, Matjaž Mikoš, Inna Novalija, Rok Orel, Ignacio Casals del Busto and Neena Goveas

Water 2026, 18(15), 1820; https://doi.org/10.3390/w18151820 - 27 Jul 2026

Viewed by 442

Abstract

Participatory citizenship plays a critical role in strengthening climate change resilience, particularly in the context of natural disasters such as floods and other hydrological extremes. Citizen-generated data shared through social media platforms offer valuable real-time insights that can complement traditional environmental monitoring systems. This study proposes a machine learning-based framework to analyze multilingual news data and global X (formerly known as Twitter) data that can complement street level sensor data for improved detection and understanding of extreme hydrological events: floods and landslides. The approach identifies and filters tweets related to hazards such as floods and contextualizes them with information extracted from news reports to enhance event characterization. In addition, sentiment and emotion analysis are applied to assess public reactions and perceived event intensity. By integrating physical event signals with societal responses, the method provides a broader perspective on disaster impacts and the effectiveness of emergency responses. The results highlight the potential of combining social media analytics and machine learning to support hydrological monitoring, enhance situational awareness, and contribute to more responsive disaster management strategies in the face of increasing climate-related risks. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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22 pages, 10897 KB

Open AccessArticle

Hybrid Projections of Nitrate Response to Hydroclimatic Variability in Selected U.S. Surface-Water and Groundwater Systems

by Bahareh KarimiDermani and Yong Zhang

Water 2026, 18(15), 1782; https://doi.org/10.3390/w18151782 - 23 Jul 2026

Viewed by 365

Abstract

Assessing nitrate sensitivity to hydroclimatic variability is important for evaluating water-quality vulnerability under hydroclimatic variations in coupled surface-water and groundwater systems. This study applies a hybrid, scenario-based projection framework combining a Long Short-Term Memory (LSTM) model and Weighted Regression on Time, Discharge, and Season with Projections (WRTDS-P) to examine nitrate plus nitrite responses under idealized wet and dry conditions across seven U.S. river basins and three associated groundwater wells. LSTM projections evaluate conditional sensitivity to altered precipitation forcing, while WRTDS-P projections assess discharge-conditioned responses based on empirically derived concentration–discharge relationships. Results show strong basin-scale heterogeneity in nitrate sensitivity. Agriculturally dominated Midwestern and central U.S. basins generally exhibit higher nitrate concentrations under the idealized wet scenarios, whereas basins influenced by groundwater buffering, flow regulation, or managed hydrology show weak or slightly negative wet–dry responses. For the three evaluated wells, groundwater projections show site-specific damped or delayed responses relative to nearby surface-water systems, reflecting aquifer storage and legacy nitrogen effects, although the Illinois well shows a larger projected wet–dry amplitude than the associated river. Cross-framework comparison reveals moderate agreement in response direction but notable differences in magnitude, highlighting sensitivity to model structure and forcing assumptions. These findings emphasize the value of hybrid projection frameworks for cross-framework sensitivity analysis and for interpreting nitrate vulnerability under hydroclimatic variation while accounting for uncertainty across surface-water and groundwater systems. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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24 pages, 6779 KB

Open AccessArticle

A Physics-Inspired Stochastic Resonance Framework for Enhancing Machine Learning Streamflow Forecasting

by Yu Quan, Chunhui Li, Xiong Zhou, Yujun Yi, Xuan Wang and Qiang Liu

Water 2026, 18(13), 1586; https://doi.org/10.3390/w18131586 - 29 Jun 2026

Viewed by 658

Abstract

Climate change introduces severe non-stationarity and high-frequency noise into hydro-meteorological data. This noise degrades the predictive accuracy of traditional data-driven streamflow models. We propose a physics-inspired data enhancement framework coupling the CEEMDAN-based Hilbert-Huang Transform (HHT) with Stochastic Resonance (SR). We applied this framework to the Lanzhou section of the upper Yellow River. HHT isolates the dominant characteristic frequency of the basin’s streamflow system at 0.0026 cycles/day. Using this frequency as a target, we constructed a Bayesian-optimized SR system. The system converts the energy of high-frequency meteorological noise into low-frequency periodic components, facilitating frequency alignment between the meteorological inputs and the hydrological response. We evaluated the SR-enhanced meteorological inputs across three machine learning architectures: Random Forest, XGBoost, and LSTM. All algorithms demonstrated an improved performance. The SR-LSTM model achieved a Nash-Sutcliffe Efficiency (NSE) of 0.91 ± 0.03. This represents a 19% improvement over the baseline LSTM score of 0.79 ± 0.02. The SR-LSTM demonstrated robust accuracy during extreme hydrological events; it achieved a high-flow NSE of 0.89 and effectively mitigated the common peak-underestimation issue by constraining relative peak magnitude errors to approximately −5.08%. Overall, this study presents a practical data enhancement approach for streamflow forecasting under complex climatic conditions. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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34 pages, 4365 KB

Open AccessArticle

Hybrid Deep Learning Models for Predicting Saltwater Intrusion in Nearshore Aquifers: Comparative Evaluation of CNN, LSTM, and DNN Architectures

by Dilip Kumar Roy, Kowshik Kumar Saha and Bithin Datta

Water 2026, 18(13), 1544; https://doi.org/10.3390/w18131544 - 24 Jun 2026

Viewed by 344

Abstract

Saltwater intrusion (SI) threatens groundwater sustainability in nearshore regions, particularly in Bangladesh, where over-extraction and sea-level rise accelerate aquifer salinization. Accurate prediction of SI dynamics is therefore critical for effective groundwater management. This study developed and evaluated several deep learning and hybrid models, including CNN, DNN, LSTM, CNN–DNN, CNN–LSTM, DNN–LSTM, and CNN–DNN–LSTM, to predict SI in a nearshore aquifer system. Predictor–response datasets were generated using the three-dimensional density-dependent flow and solute transport model FEMWATER. This study presents the first comprehensive benchmarking of standalone and hybrid CNN–DNN–LSTM models for SI prediction in a Bangladesh nearshore aquifer, supported by CRITIC–EDAS-based model ranking. Model performance was assessed using RMSE, MAE, MAD, R, IOA, a-20, NRMSE, along with CRITIC weighting and EDAS ranking. Results indicate that hybrid models integrating LSTM outperformed standalone models. The CNN–LSTM model achieved the best performance at OW1 (RMSE = 1.57 mg/L, MAE = 1.26 mg/L, R = 0.99, IOA = 0.99). The DNN–LSTM model performed best at OW2 (RMSE = 2.87 mg/L, IOA = 0.98, R = 0.97) and OW3 (RMSE = 1.95 mg/L, IOA = 0.99, R = 0.99). In contrast, the DNN model showed poor performance, while the CNN model demonstrated moderate performance and the LSTM model underperformed. Overall, the hybrid CNN–LSTM and DNN–LSTM models demonstrated superior accuracy and robustness for reliable SI prediction and sustainable groundwater management. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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21 pages, 6011 KB

Open AccessArticle

Urban Runoff Pollution Forecasting in the Yangtze River Basin: A Physics-Informed Data-Driven Framework Enhanced with Cluster-Based Transfer Learning

by Yacheng Sun, Yasong Chen, Yuzhen Li, Tingting Li and Wenlong Zhang

Water 2026, 18(9), 1095; https://doi.org/10.3390/w18091095 - 2 May 2026

Viewed by 1238

Abstract

Accurate forecasting of urban rainfall-runoff pollution across large river basins is essential for urban water management. However, this task faces formidable challenges due to the scarcity of locally monitored data and the heterogeneity in hydrological and pollution processes. To address these challenges, we proposed a novel three-tiered framework comprising (1) functional area clustering using 16-dimensional features to identify zones with shared pollution mechanisms and establish a physical parameter library; (2) a hybrid physics-informed data-driven model integrating SWMM with a Residual-BiLSTM-Multi-Head Attention (RLA) model; and (3) cluster-based transfer learning enabling predictions in data-scarce zones. The framework’s efficacy was demonstrated through a multi-tiered dataset for the Yangtze River Basin. First, a knowledge base comprising 2390 reported rainfall events across 57 functional areas was synthesized to inform the functional clustering and establish a shared physical parameter library. Subsequently, intensive field monitoring from two representative residential areas was used to train and validate the hybrid model. In data-rich zones within a cluster, the model achieved high accuracy (R² > 0.82). For data-scarce zones within the same functional cluster, the model maintained a promising performance (R² > 0.5). This study presents a novel basin-scale framework, with its initial application and preliminary validation in the Yangtze River Basin. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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18 pages, 11149 KB

Open AccessArticle

LRES-YOLO: Target Detection Algorithm for Landslides on Reservoir Embankment Slopes

by Xiaohua Xu, Xuecai Bao, Zhongxi Wang, Haijing Wang and Xin Wen

Water 2026, 18(8), 889; https://doi.org/10.3390/w18080889 - 8 Apr 2026

Viewed by 763

Abstract

To address the urgent need for enhancing landslide risk monitoring in reservoir embankment slopes, a core component of water conservancy projects, this paper proposes the LRES-YOLO algorithm for real-time landslide detection on reservoir embankments. In LRES-YOLO, we first integrate coordinate attention into basic feature extraction convolutional blocks to form the CACBS attention module, which enhances the model’s ability to identify and locate landslide targets in complex reservoir terrain, overcoming positional information insensitivity in deep networks. Second, we add novel downsampling DP modules and ELAN-W modules to the backbone network, improving feature recognition efficiency for embankment slopes with diverse hydrological and topographical interference. Third, we optimize the feature fusion network with targeted concatenation and pooling operations, balancing semantic information enhancement with computational load reduction to mitigate overfitting in variable reservoir environments. Finally, we adopt Focal Loss and EIoU Loss to accelerate training convergence and strengthen target feature representation for small or obscured landslides on embankments. Experimental results show that LRES-YOLO outperforms traditional algorithms in detecting landslides across diverse reservoir embankment scenarios: it achieves an average improvement of 8.4 percentage points in mean mAP over the best-performing baseline across five independent trials, a detection speed of 8.2 ms per image, and memory usage of 139 MB. This lightweight design makes it suitable for edge computing devices, providing robust technical support for intelligent monitoring systems in water conservancy projects. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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23 pages, 13691 KB

Open AccessArticle

Deep Learning-Based Enhancement for Surface Velocity Measurements in Tidal Estuaries

by Wei-Che Huang, Whita Wulansari, Suharyanto and Wen-Cheng Liu

Water 2026, 18(4), 468; https://doi.org/10.3390/w18040468 - 11 Feb 2026

Viewed by 958

Abstract

Accurate estimation of river surface velocity is essential for hydrological monitoring and flood management. However, conventional Large-Scale Particle Image Velocimetry (LSPIV) is often affected by errors arising from inaccurate Region of Interest (ROI) delineation and interference from floating objects or vessels. To overcome these limitations, this study integrates LSPIV with two deep learning models, SegNet and YOLOv8, to enable automated ROI segmentation and vessel detection. SegNet performs real-time identification of water body regions, while YOLOv8 detects and removes vessel intrusions within the ROI, thereby enhancing the precision of velocity estimation. Six field experiments were conducted to assess the performance of the proposed system. The deep learning-enhanced LSPIV achieved Root Mean Square Error (RMSE) values ranging from 0.048 to 0.11 m/s and Normalized RMSE (NRMSE) values between 3.53% and 10.34%, with coefficients of determination (R²) exceeding 0.895 when compared with Acoustic Doppler Current Profiler (ADCP) measurements. SegNet-based ROI segmentation reduced RMSE by up to 0.046 m/s andNRMSE by up to 3.44%, and improved R² by up to 0.012, while image enhancement further improved segmentation accuracy under varying illumination conditions. Moreover, YOLOv8 successfully detected all vessel intrusions observed in this study, thereby reducing the discrepancies between LSPIV and ADCP-derived velocities from 0.032–0.345 m/s to 0.022–0.314 m/s. Overall, the integration of LSPIV with SegNet and YOLOv8 establishes a highly automated and accurate framework for river surface velocity estimation, demonstrating strong potential for real-time hydrological monitoring and flood risk assessment. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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24 pages, 20378 KB

Open AccessArticle

Water Functional Zoning Framework Based on Machine Learning: A Case Study of the Yangtze River Basin

by Wei Liu, Yuanzhuo Sun, Fuliang Deng, Bo Wu, Xiaoyan Zhang, Mei Sun, Lanhui Li, Hui Li and Ying Yuan

Water 2026, 18(2), 209; https://doi.org/10.3390/w18020209 - 13 Jan 2026

Viewed by 659

Abstract

Water functional zoning plays a crucial role in water resource allocation, pollution prevention, and ecological protection. With the increasing intensity of human activities, there is a significant mismatch between current water functional zoning and the economic, social development needs and ecological protection goals. Existing water functional zoning methods mainly rely on expert experience for qualitative judgment, which is highly subjective and inefficient. In response, this paper presents a transferable quantitative feature system and introduces a machine learning-based progressive zoning framework for water functions, validated through a case study of the Yangtze River Basin. The results show that the overall accuracy of the framework is 0.78, which is 4–7% higher compared to traditional single models. In terms of spatial distribution, the transformation of protection and reserved zones in 2020 mainly occurred in the middle and lower reaches, where human activities are frequent, particularly in Sichuan and Jiangxi provinces. The development zones are highly concentrated in the downstream areas, with some regions transitioning into protection or reserved zones, mainly in Hubei and Chongqing provinces. Adjustments to buffer zones are primarily concentrated along inter-provincial boundary areas, such as the junction between Hubei and Anhui provinces. This framework helps managers quickly identify key areas for optimizing water functional zones, providing valuable reference for the precise management of water resources and the formulation of ecological protection strategies in the basin. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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26 pages, 3456 KB

Open AccessArticle

Multi-Scale and Interpretable Daily Runoff Forecasting with IEWT and ModernTCN

by Qing Li, Yunwei Zhou, Yongshun Zheng, Chu Zhang and Tian Peng

Water 2026, 18(2), 183; https://doi.org/10.3390/w18020183 - 9 Jan 2026

Cited by 2 | Viewed by 796

Abstract

Daily runoff series exhibit high complexity and significant fluctuations, which often lead to large prediction errors and limit the scientific basis of water resource scheduling and management. This study proposes a runoff prediction framework that incorporates upstream–downstream hydrological correlation information and integrates Improved Empirical Wavelet Transform (IEWT), SHAP-based interpretable feature selection, Improved Population-Based Training (IPBT), and the Modern Temporal Convolutional Network (ModernTCN) to enhance forecasting accuracy and model robustness. First, IEWT is employed to perform multi-scale decomposition of the daily runoff sequence, extracting structural features at different temporal scales. Then, upstream–downstream hydrological correlation information is introduced, and the SHAP method is used to evaluate the importance of multi-source basin features, eliminating redundant variables to improve input quality and training efficiency. Finally, IPBT is applied to optimize ModernTCN hyperparameters, thereby constructing a high-performance forecasting model. Case studies at the Hankou station demonstrate that the proposed IPBT-IEWT-SHAP-ModernTCN model significantly outperforms benchmark methods such as LSTM, iTransformer, and TCN in terms of accuracy, stability, and generalization. Specifically, the model achieves a root mean square error of 342.14, a mean absolute error of 251.01, and a Nash–Sutcliffe efficiency of 0.9992. These results indicate that the proposed method can effectively capture the nonlinear correlation characteristics between upstream and downstream hydrological processes, thus providing an efficient and widely adaptable framework for daily runoff prediction and scientific water resources management. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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Review

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17 pages, 665 KB

Open AccessReview

Advancing Water Quality Monitoring in eThekwini, South Africa: Integrating Water 4.0, Automation, and AI for Real-Time Surveillance

by Owen Rubaba and Tom Walingo

Water 2025, 17(22), 3299; https://doi.org/10.3390/w17223299 - 18 Nov 2025

Viewed by 3017

Abstract

Global strategies for ensuring access to clean and safe drinking water are increasingly shifting toward a preventive approach based on risk assessment and risk management of the entire water supply and production chain. However, many developing countries, including South Africa, still lag in adopting advanced real-time water monitoring technologies aligned with Water 4.0 principles. To transition to these innovative technologies, it is essential to understand current gaps in water monitoring and the challenges to adopting these systems. This systemic review aims to assess current monitoring practices, identify implementation challenges, and explore strategic pathways for adopting smart water infrastructure in eThekwini Municipality, South Africa. This review identifies critical gaps in eThekwini’s water quality monitoring, including limited real-time surveillance, fragmented data systems, budgetary constraints, cybersecurity vulnerabilities, uneven rural–urban access, slow commercialization of academic innovations, policy misalignment, and insufficient technical capacity. It emphasizes the potential of real-time monitoring systems, automation, and artificial intelligence (AI) to address existing water quality monitoring challenges. Additionally, special focus is given to the role of electronic sensors in measuring physicochemical parameters like turbidity, pH, and dissolved oxygen as cost-effective indicators for detecting microbial contaminants. Implementing Water 4.0 strategies provides eThekwini and similar municipalities an opportunity to develop a more proactive, resilient, and sustainable approach to water quality management. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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17 pages, 2889 KB

Open AccessTechnical Note

Increasing Computational Efficiency of a River Ice Model to Help Investigate the Impact of Ice Booms on Ice Covers Formed in a Regulated River

by Karl-Erich Lindenschmidt, Mojtaba Jandaghian, Saber Ansari, Denise Sudom, Sergio Gomez, Stephany Valarezo Plaza, Amir Ali Khan, Thomas Puestow and Seok-Bum Ko

Water 2026, 18(2), 218; https://doi.org/10.3390/w18020218 - 14 Jan 2026

Viewed by 1201

Abstract

The formation and stability of river ice covers in regulated waterways are critical for uninterrupted hydro-electric operations. This study investigates the modelling of ice cover development in the Beauharnois Canal along the St. Lawrence River with the presence and absence of ice booms. Ice booms are deployed in this canal to promote the rapid formation of a stable ice cover during freezing events, minimizing disruptions to dam operations. Remote sensing data were used to assess the spatial extent and temporal evolution of an ice cover and to calibrate the river ice model RIVICE. The model was applied to simulate ice formation for the 2019–2020 ice season, first for the canal with a series of three ice booms and then rerun under a scenario without booms. Comparative analysis reveals that the presence of ice booms facilitates the development of a relatively thinner and more uniform ice cover. In contrast, the absence of booms leads to thicker ice accumulations and increased risk of ice jamming, which could impact water management and hydroelectric generation operations. Computational efficiencies of the RIVICE model were also sought. RIVICE was originally compiled with a Fortran 77 compiler, which restricted modern optimization techniques. Recompiling with NVFortran significantly improved performance through advanced instruction scheduling, cache management, and automatic loop analysis, even without explicit optimization flags. Enabling optimization further accelerated execution, albeit marginally, reducing redundant operations and memory traffic while preserving numerical integrity. Tests across varying ice cross-sectional spacings confirmed that NVFortran reduced runtimes by roughly an order of magnitude compared to the original model. A test GPU (Graphics Processing Unit) version was able to run the data interpolation routines on the GPU, but frequent data transfers between the CPU (Central Processing Unit) and GPU caused by shared memory blocks and fixed-size arrays made it slower than the original CPU version. Achieving efficient GPU execution would require substantial code restructuring to eliminate global states, adopt persistent data regions, and parallelize at higher level loops, or alternatively, rewriting in a GPU-friendly language to fully exploit modern architectures. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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