Sustainability

Research

26 pages, 9262 KB

Open AccessArticle

Multi-Actor Conflict Identification and Governance Optimization in Urban Water-Ecological Systems Based on Knowledge Graph and Complex Networks

by Jiaming Xu, Zhao Xu and Guangyao Chen

Sustainability 2026, 18(10), 4721; https://doi.org/10.3390/su18104721 - 9 May 2026

Viewed by 243

Abstract

Urban water-ecological governance in the Yellow River Basin is shifting from a single administratively dominated model toward a polycentric collaborative system. However, ambiguous responsibilities and overlapping tasks among governments, enterprises, and society often lead to governance conflicts, reduced coordination efficiency, and growing risks [...] Read more.

Urban water-ecological governance in the Yellow River Basin is shifting from a single administratively dominated model toward a polycentric collaborative system. However, ambiguous responsibilities and overlapping tasks among governments, enterprises, and society often lead to governance conflicts, reduced coordination efficiency, and growing risks to regional ecological security. To address this challenge, this study develops a multi-actor governance analysis framework integrating deep learning, knowledge graphs, and complex network optimization. Stakeholder demands are extracted from multi-source data using a BERT-BiLSTM-CRF model, including policy documents, enterprise reports, and public discourse, and are then organized into a knowledge graph for water-ecological governance. A Relational Graph Attention Network (R-GAT) is subsequently used to transform the knowledge graph into a signed weighted network, enabling the measurement of conflict intensity and the identification of key conflict nodes across governance scenarios. Based on multi-objective optimization, a Pareto frontier is constructed to balance conflict tension, fairness, and governance efficiency, from which a compromise solution for responsibility weighting is identified. An empirical case study of a typical city in the Yellow River Basin shows that the proposed framework can identify core conflict nodes and provide quantitative support for conflict mitigation and coordination adjustment. The findings offer a quantitative reference for institutional innovation and evidence-based decision-making in urban water-ecological governance. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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20 pages, 17767 KB

Open AccessArticle

Investigation of the Optimal Scheduling Strategy for an Intake Pump Station Based on Surrogate Models of the Differential Evolution Algorithm

by Xuecong Qin, Yin Luo and Yujie Gu

Sustainability 2026, 18(10), 4691; https://doi.org/10.3390/su18104691 - 8 May 2026

Viewed by 215

Abstract

At the Second Water Intake Pump Station of the Chenhang Reservoir in Shanghai, suboptimal pump scheduling resulted in electricity consumption cost attributable to pump-motor equipment accounting for an exceptionally large proportion of the total power expenditure. In response to the economical operation issues, [...] Read more.

At the Second Water Intake Pump Station of the Chenhang Reservoir in Shanghai, suboptimal pump scheduling resulted in electricity consumption cost attributable to pump-motor equipment accounting for an exceptionally large proportion of the total power expenditure. In response to the economical operation issues, a mathematical model of power consumption cost for the pump station was established by introducing time-of-use electricity pricing and constraint suppression terms. Taking the minimum cost as the research objective, the differential evolution (DE) algorithm was employed to establish a fitness function for electricity cost, aiming to find the most economical and reliable scheduling strategy. However, owing to its low computational speed and high complexity, machine learning was introduced to establish neural network surrogate models of the DE algorithm. By comparing three surrogate models, the Multilayer Perceptron (MLP) neural network model was adopted as the most appropriate surrogate model. It was optimized for robustness improvement and verified on site. The results demonstrate that implementing the surrogate model achieves over 25% savings in electricity cost per thousand cubic meters of water, while slashing the solution time by 88.53% compared to the standard DE algorithm. Furthermore, the overall power consumption is reduced by 2.20% under a cost-priority strategy and by 15.89% under a power-priority strategy, thereby directly mitigating the carbon footprint of the pump station. The proposed hybrid computational framework in this study bridges the gap between the computationally expensive heuristic optimization and the strict real-time control requirements in engineering, highlighting its significant contribution to the sustainable and low-carbon operation of water infrastructure. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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28 pages, 18007 KB

Open AccessArticle

Revitalizing Water Storage Capacity: Remote Sensing and Optimization-Based Design for a New Dam

by Ömer Genç, Latif Onur Uğur, Rıfat Akbıyıklı, Beytullah Bozali and Volkan Ateş

Sustainability 2026, 18(7), 3312; https://doi.org/10.3390/su18073312 - 29 Mar 2026

Viewed by 446

Abstract

Most of the dam structures around the world are approaching the end of their economic life of 50 to 70 years, especially due to sediment accumulation in reservoir areas. This situation necessitates the development of proactive infrastructure management strategies. This study presents an [...] Read more.

Most of the dam structures around the world are approaching the end of their economic life of 50 to 70 years, especially due to sediment accumulation in reservoir areas. This situation necessitates the development of proactive infrastructure management strategies. This study presents an original framework for the process of renewal of aging dams that blends remote sensing techniques and meta-intuitive optimization methods. Within the scope of the study, the Hasanlar Dam located in Düzce was selected as a sample, and a new dam axis was determined in the upper part of the basin. A detailed volume–height curve was created using 12.5 m resolution ALOS PALSAR numerical height models (DEM) and GIS-based spatial data curation to calculate the reservoir storage capacity in precise increments of 2 m. To maximize the structural efficiency of the proposed “New Hasanlar Dam”, the cross-sectional area has been minimized through seven current algorithms such as Genetic Algorithm (GA), Arithmetic Optimization Algorithm (AOA), Gray Wolf Optimizer (GWO), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Crayfish Optimization Algorithm (CAO), and Cheetah Optimizer (CO). The findings obtained prove that the PSO and CAOs achieved a significant reduction in cross-sectional area by 29.36% and successfully approached the global optimum. The replacement of the 55.5 million m³ capacity of the existing Hasanlar Dam with a new structure with a height of 78 m will guarantee sustainability and structural safety in water management. As a result, this study reveals that the integration of high-resolution remote sensing data and advanced heuristic methods is a cost-effective and powerful tool in the strategic renovation of aging hydraulic infrastructures. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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20 pages, 2659 KB

Open AccessArticle

Assessing WQI Using Spatial Land-Use Context Derived from Google Earth Imagery and Advanced Convolutional Neural Networks in South Korea

by Inho Choi, Jong Hwan Kim, Sangdon Lee, Jooyoung Park and Jong-Min Oh

Sustainability 2026, 18(5), 2377; https://doi.org/10.3390/su18052377 - 1 Mar 2026

Viewed by 410

Abstract

Assessing water quality indices (WQIs) derived from physicochemical measurements accurately and efficiently is essential for effective water resource management. However, conventional monitoring approaches based on single-point measurements and limited spatial coverage face constraints in representing large-scale river environments. To address these limitations, this [...] Read more.

Assessing water quality indices (WQIs) derived from physicochemical measurements accurately and efficiently is essential for effective water resource management. However, conventional monitoring approaches based on single-point measurements and limited spatial coverage face constraints in representing large-scale river environments. To address these limitations, this study integrates high-resolution Google Earth RGB imagery with national water quality monitoring data from South Korea to construct an image-based dataset for WQI estimation. Water quality monitoring records from 1762 sampling sites collected between January 2000 and September 2020 were used to calculate WQI values. The index was computed using seven parameters—temperature, pH, dissolved oxygen, total solids, biochemical oxygen demand, nitrate, and phosphate—following the standard weighting procedure. Corresponding Google Earth RGB imagery acquired within ±1 day of field measurements over the same 2000–2020 period was compiled, resulting in 34108 image–sample pairs. Based on this integrated dataset, a ResNeXt-based convolutional neural network enhanced with convolutional block attention modules was implemented and applied to estimate WQI values from spatial land-use context and river morphology captured in RGB imagery. The proposed model demonstrated superior predictive performance compared to baseline neural network models, achieving a coefficient of determination (R²) of 0.94 and an index of agreement (IOA) of 0.96. Grad-CAM analysis indicates that the model primarily utilizes spatial land-use patterns, riparian context, and river morphology rather than direct visual signals from the water surface itself. These findings suggest that RGB imagery contains spatial information related to observed WQI values. Accordingly, the framework provides a spatially continuous perspective on river conditions that may support large-scale monitoring efforts. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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32 pages, 10174 KB

Open AccessArticle

Performance Evaluation and Model Validation of Conventional Solar Still in Harsh Summer Climate: Case Study of Basrah, Iraq

by Mohammed Oudah Khalaf, Mehmed Rafet Özdemir and Hussein Sadiq Sultan

Sustainability 2026, 18(1), 479; https://doi.org/10.3390/su18010479 - 2 Jan 2026

Cited by 2 | Viewed by 927

Abstract

Freshwater scarcity is a critical global challenge, particularly in arid and semi-arid regions like southern Iraq. This study evaluates the thermal and distillate performance of a conventional single-slope solar still under extreme summer conditions in Basrah, Iraq. The objective is to analyze and [...] Read more.

Freshwater scarcity is a critical global challenge, particularly in arid and semi-arid regions like southern Iraq. This study evaluates the thermal and distillate performance of a conventional single-slope solar still under extreme summer conditions in Basrah, Iraq. The objective is to analyze and validate a coupled theoretical–experimental model for predicting temperature fields and freshwater productivity. The model incorporates transient energy and mass balance equations with temperature- and salinity-dependent thermophysical properties. Experiments were conducted using brackish water from the Shatt al-Arab River (salinity: 5.2 g/kg), and measured temperatures and productivity were compared against simulations over a 24-h period. Strong agreement was achieved between experimental and theoretical results, with

R^{2} > 0.90

for temperature predictions and

R^{2} = 0.985

for hourly productivity. Maximum hourly yield reached

0.46 L / m^{2}

, with a total daily productivity of

3.5 L / m^{2}

, The daily thermal efficiency was found to be 26.90% experimentally and 28.20% theoretically. A positive linear relation between the thermal gradient

(T_{w} - T_{g})

and hourly productivity was also established. The findings confirm the reliability of the developed model and highlight the potential of solar distillation as a sustainable freshwater source for high-temperature regions. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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19 pages, 1815 KB

Open AccessArticle

Diffusion-Driven Time-Series Forecasting to Support Sustainable River Ecosystems and SDG-Aligned Water-Resource Governance in Thailand

by Weenuttagant Rattanatheerawon and Rerkchai Fooprateepsiri

Sustainability 2025, 17(22), 10295; https://doi.org/10.3390/su172210295 - 18 Nov 2025

Viewed by 1319

Abstract

Time-series water-quality forecasting plays a crucial role in sustainable environmental monitoring, early-warning surveillance, and data-driven water-resource governance. Degradation of river ecosystems poses significant risks to public health, biodiversity, and long-term socio-economic resilience, particularly in rapidly developing regions. In this study, a multi-scale diffusion [...] Read more.

Time-series water-quality forecasting plays a crucial role in sustainable environmental monitoring, early-warning surveillance, and data-driven water-resource governance. Degradation of river ecosystems poses significant risks to public health, biodiversity, and long-term socio-economic resilience, particularly in rapidly developing regions. In this study, a multi-scale diffusion forecaster (MDF) is introduced to enhance predictive accuracy and uncertainty quantification for river water-quality dynamics in Thailand. The proposed framework integrates seasonal-trend decomposition with a hierarchical denoising diffusion process to model stochastic environmental fluctuations across multiple temporal resolutions. Experiments conducted using real water-quality datasets from the Mae Klong, Khwae Noi, and Khwae Yai Rivers, and the Port Authority of Thailand, demonstrate that MDF achieves superior probabilistic calibration under noise and data incompleteness compared to conventional deterministic baselines. The forecasting capability supports proactive pollution control, sustainable resource allocation, and climate-resilient water-policy design, directly contributing to Sustainable Development Goals (SDG 6: Clean Water and Sanitation; SDG 13: Climate Action; and SDG 14: Life Below Water). The findings highlight the potential of diffusion-based learning as an enabling technology for sustainable aquatic ecosystem governance and long-term environmental planning. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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17 pages, 4074 KB

Open AccessArticle

Groundwater Level Prediction Using a Hybrid TCN–Transformer–LSTM Model and Multi-Source Data Fusion: A Case Study of the Kuitun River Basin, Xinjiang

by Yankun Liu, Mingliang Du, Xiaofei Ma, Shuting Hu and Ziyun Tuo

Sustainability 2025, 17(19), 8544; https://doi.org/10.3390/su17198544 - 23 Sep 2025

Cited by 2 | Viewed by 1918

Abstract

Groundwater level (GWL) prediction in arid regions faces two fundamental challenges in conventional numerical modeling: (i) irreducible parameter uncertainty, which systematically reduces predictive accuracy; (ii) oversimplification of nonlinear process interactions, which leads to error propagation. Although machine learning (ML) methods demonstrate strong nonlinear [...] Read more.

Groundwater level (GWL) prediction in arid regions faces two fundamental challenges in conventional numerical modeling: (i) irreducible parameter uncertainty, which systematically reduces predictive accuracy; (ii) oversimplification of nonlinear process interactions, which leads to error propagation. Although machine learning (ML) methods demonstrate strong nonlinear mapping capabilities, their standalone applications often encounter prediction bias and face the accuracy–generalization trade-off. This study proposes a hybrid TCN–Transformer–LSTM (TTL) model designed to address three key challenges in groundwater prediction: high-frequency fluctuations, medium-range dependencies, and long-term memory effects. The TTL framework integrates TCN layers for short-term features, Transformer blocks to model cross-temporal dependencies, and LSTM to preserve long-term memory, with residual connections facilitating hierarchical feature fusion. The results indicate that (1) at the monthly scale, TTL reduced RMSE by 20.7% (p < 0.01) and increased R² by 0.15 compared with the Groundwater Modeling System (GMS); (2) during abrupt hydrological events, TTL achieved superior performance (R² = 0.96–0.98, MAE < 0.6 m); (3) PCA revealed site-specific responses, corroborating the adaptability and interpretability of TTL; (4) Grad-CAM analysis demonstrated that the model captures physically interpretable attention mechanisms—particularly evapotranspiration and rainfall—thereby providing clear cause–effect explanations and enhancing transparency beyond black-box models. This transferable framework supports groundwater forecasting, risk warning, and practical deployment in arid regions, thereby contributing to sustainable water resource management. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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20 pages, 4138 KB

Open AccessArticle

Sustainability Assessment of Harvesting Rainwater and Air-Conditioning Condensate Water in Multi-Family Residential Buildings under Various Conditions in Israel—A Simulation Study

by Tamar Opher and Eran Friedler

Sustainability 2024, 16(19), 8369; https://doi.org/10.3390/su16198369 - 26 Sep 2024

Cited by 2 | Viewed by 2987

Abstract

The environmental impacts and water savings of different configurations of non-potable domestic water use (toilet flushing and laundry), sourced from rainwater harvesting (RWH) and air-conditioning condensate water (ACWH), in multi-family buildings in Israel are examined. Two building types differing in specific roof areas, [...] Read more.

The environmental impacts and water savings of different configurations of non-potable domestic water use (toilet flushing and laundry), sourced from rainwater harvesting (RWH) and air-conditioning condensate water (ACWH), in multi-family buildings in Israel are examined. Two building types differing in specific roof areas, and three climatic sub-regions were modeled. RWH satisfied 23 and 46% of the water demand for toilet flushing and laundry in high-rise and low-rise buildings, respectively. Air conditioning is used almost daily during Israel’s hot and dry summers. Hence, the combined RWH-ACWH system saved 42 and 64% in high- and low-rise buildings, respectively. Displacing desalinated seawater, a significant water source in Israel, with alternative water sources lowered the environmental impacts with an increase in storage, up to a certain volume, beyond which impacts started rising. The same infrastructure is used during winter for RWH and for ACWH during summer; thus, combining the two exhibits significant water savings, with marginal extra costs while lowering the environmental impacts. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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18 pages, 3584 KB

Open AccessArticle

Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data

by Ahmet Cemkut Badem, Recep Yılmaz, Muhammet Raşit Cesur and Elif Cesur

Sustainability 2024, 16(17), 7696; https://doi.org/10.3390/su16177696 - 4 Sep 2024

Cited by 5 | Viewed by 2591

Abstract

Dams significantly impact the environment, industries, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy [...] Read more.

Dams significantly impact the environment, industries, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy levels precisely to contribute to sustainable water management by enabling efficient water allocation among sectors, proactive drought management, controlled flood risk mitigation, and preservation of downstream ecological integrity. Our research suggests that combining physical models of water inflow and outflow “such as evapotranspiration using the Penman–Monteith equation, along with parameters like water consumption, solar radiation, and rainfall” with data-driven models based on historical reservoir data is crucial for accurately predicting occupancy levels. We implemented various prediction models, including Random Forest, Extra Trees, Long Short-Term Memory, Orthogonal Matching Pursuit CV, and Lasso Lars CV. To strengthen our proposed model with robust evidence, we conducted statistical tests on the mean absolute percentage errors of the models. Consequently, we demonstrated the impact of physical model parameters on prediction performance and identified the best method for predicting dam occupancy levels by comparing it with findings from the scientific literature. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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22 pages, 7626 KB

Open AccessArticle

An Improved Aggregation–Decomposition Optimization Approach for Ecological Flow Supply in Parallel Reservoir Systems

by Inkyung Min, Nakyung Lee, Sanha Kim, Yelim Bang, Juyeon Jang, Kichul Jung and Daeryong Park

Sustainability 2024, 16(17), 7475; https://doi.org/10.3390/su16177475 - 29 Aug 2024

Cited by 2 | Viewed by 1573

Abstract

The efficient operation of multi-reservoirs is highly beneficial for securing supply for prevailing demand and ecological flow. This study proposes a monthly hedging rule-based aggregation–decomposition model for optimizing a parallel reservoir system. The proposed model, which is an aggregated hedging rule for ecological [...] Read more.

The efficient operation of multi-reservoirs is highly beneficial for securing supply for prevailing demand and ecological flow. This study proposes a monthly hedging rule-based aggregation–decomposition model for optimizing a parallel reservoir system. The proposed model, which is an aggregated hedging rule for ecological flow (AHRE), uses external optimization to determine the total release of the reservoir system based on improved hedging rules—the optimization model aims to minimize water demand and ecological flow deficits. Additionally, inner optimization distributes the release to individual reservoirs to maintain equal reservoir storage rates. To verify the effectiveness of the AHRE, a standard operation policy and transformed hedging rules were selected for comparison. Three parallel reservoirs in the Naesung Stream Basin in South Korea were selected as a study area. The results of this study demonstrate that the AHRE is better than the other two methods in terms of supplying water in line with demand and ecological flow. In addition, the AHRE showed relatively stable operation results with small water-level fluctuations, owing to the application of improved hedging rules and a decomposition method. The results indicate that the AHRE has the capacity to improve downstream river ecosystems while maintaining human water use and provide a superior response to uncertain droughts. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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22 pages, 9241 KB

Open AccessArticle

Research on Air Quality in Response to Meteorological Factors Based on the Informer Model

by Xiaoqing Tian, Chaoqun Zhang, Huan Liu, Baofeng Zhang, Cheng Lu, Pengfei Jiao and Songkai Ren

Sustainability 2024, 16(16), 6794; https://doi.org/10.3390/su16166794 - 8 Aug 2024

Cited by 10 | Viewed by 3070

Abstract

The quality of the air exerts considerable effects on human health, and meteorological factors affect air quality. The relationships between meteorological factors and air quality parameters are complex dependency correlations. This article is based on the air quality monitoring data and meteorological monitoring [...] Read more.

The quality of the air exerts considerable effects on human health, and meteorological factors affect air quality. The relationships between meteorological factors and air quality parameters are complex dependency correlations. This article is based on the air quality monitoring data and meteorological monitoring data obtained from a monitoring station in Binjiang District, Hangzhou City, China, spanning from 01:00 on 14 April 2021 to 23:00 on 31 December 2021. The Informer model was used to explore the air quality parameters’ response to meteorological factors. By analyzing 12 different kinds of 2-Minute Average Wind Speed (2-MAWSP), 10-Minute Average Wind Speed (10-MAWSP), and Maximum Wind Speed (MXSPD); 16 different kinds of Hourly Precipitation (HP) and Air Temperature (AT); 11 different kinds of Relative Humidity (RH); and 8 different kinds of Station Pressure (STP), the following results were obtained: (1) The influence of wind speed on various air quality parameters is multifaceted and lacks a standardized form, potentially influenced by factors like wind direction and geographical location. One clear effect of wind speed is evident in the levels of particulate matter 10 (with an aerodynamic diameter smaller than 10 μm, PM₁₀), as the values of this parameter first decrease and then increase with increasing wind speed. (2) HP has an evident reducing effect on most air quality parameters, including particulate matter (including PM_2.5 and PM₁₀), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂), as well as nitrogen oxides (NO_x). (3) The increase in AT has a clear reducing effect on the concentration of NO₂, while the trend for the concentrations of PM₁₀ and NO_x is one of initial decrease followed by a gradual rise. (4) RH only reduces the concentrations of SO₂ and PM₁₀. (5) With the rise in STP, the concentrations of most air quality parameters generally rise as well, except for the decrease in NO_x concentration. This can give some indications and assistance to meteorological and environmental departments for improving air quality. This model can be used for a performance analysis and the forecasting of multi-parameter non-correlated systems. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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15 pages, 2799 KB

Open AccessArticle

A Comparative Analysis of Advanced Machine Learning Techniques for River Streamflow Time-Series Forecasting

by Antoifi Abdoulhalik and Ashraf A. Ahmed

Sustainability 2024, 16(10), 4005; https://doi.org/10.3390/su16104005 - 10 May 2024

Cited by 4 | Viewed by 3044

Abstract

This study examines the contribution of rainfall data (RF) in improving the streamflow-forecasting accuracy of advanced machine learning (ML) models in the Syr Darya River Basin. Different sets of scenarios included rainfall data from different weather stations located in various geographical locations with [...] Read more.

This study examines the contribution of rainfall data (RF) in improving the streamflow-forecasting accuracy of advanced machine learning (ML) models in the Syr Darya River Basin. Different sets of scenarios included rainfall data from different weather stations located in various geographical locations with respect to the flow monitoring station. Long short-term memory (LSTM)-based models were used to examine the contribution of rainfall data on streamflow-forecasting performance by investigating five scenarios whereby RF data from different weather stations were incorporated depending on their geographical positions. Specifically, the All-RF scenario included all rainfall data collected at 11 stations; Upstream-RF (Up-RF) and Downstream-RF (Down-RF) included only the rainfall data measured upstream and downstream of the streamflow-measuring station; Pearson-RF (P-RF) only included the rainfall data exhibiting the highest level of correlation with the streamflow data, and the Flow-only (FO) scenario included streamflow data. The evaluation metrics used to quantitively assess the performance of the models included the RMSE, MAE, and the coefficient of determination, R². Both ML models performed best in the FO scenario, which shows that the diversity of input features (hydrological and meteorological data) did not improve the predictive accuracy regardless of the positions of the weather stations. The results show that the P-RF scenarios yielded better prediction accuracy compared to all the other scenarios including rainfall data, which suggests that only rainfall data upstream of the flow monitoring station tend to make a positive contribution to the model’s forecasting performance. The findings evidence the suitability of simple monolayer LSTM-based networks with only streamflow data as input features for high-performance and budget-wise river flow forecast applications while minimizing data processing time. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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Review

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25 pages, 2127 KB

Open AccessReview

Impact of Artificial Intelligence on the Sustainable Use of Water Resources

by Jonathan Alexander Ruiz Carrillo, Olger Huamaní Jordan, Eddy Gregorio Mendoza Loor and Cristian Xavier Espín Beltrán

Sustainability 2026, 18(8), 3864; https://doi.org/10.3390/su18083864 - 14 Apr 2026

Viewed by 654

Abstract

This bibliometric study examines artificial intelligence’s impact on sustainable water management through systematic analysis of 424 publications from Scopus, Web of Science, and IEEE Xplore following the 2020 PRISMA guidelines. Four analytical approaches were implemented: descriptive bibliometric characterization, VOSviewer network visualization, principal component [...] Read more.

This bibliometric study examines artificial intelligence’s impact on sustainable water management through systematic analysis of 424 publications from Scopus, Web of Science, and IEEE Xplore following the 2020 PRISMA guidelines. Four analytical approaches were implemented: descriptive bibliometric characterization, VOSviewer network visualization, principal component analysis with Ward’s hierarchical clustering (86.58% variance explained, cophenetic correlation = 0.951), and qualitative synthesis. The results reveal exponential growth from 4 publications (2018) to 167 (2025) with geographic concentration in China (30.2%), the USA (9.7%), and India (8.0%). Collaboration networks exhibit pronounced fragmentation (density = 0.04, modularity = 0.78) with minimal North–South partnerships (12%). Critically, keyword analysis identifies five thematic clusters dominated by machine learning methodologies, whereas governance and equity dimensions appear fewer than eight times, revealing a fundamental gap wherein technical optimization proceeds without the institutional frameworks necessary for equitable water access. Multivariate analysis suggests that technological infrastructure capacity is a stronger correlate of research output than geographic water stress, based on the observed geographic distribution of high-output nations rather than direct operationalization of scarcity indicators. The qualitative synthesis revealed that 68% of the studies remained pilot-scale studies, 82% were concentrated in developed nations, and 66% cited data quality as the primary constraint. The bibliometric patterns suggest a pronounced orientation toward computational approaches, alongside paradoxical AI infrastructure water consumption that may partially offset conservation benefits. (Note: 2025 figures reflect early-access articles retrieved before the November 2024 search date and should be interpreted as partial-year estimates.) Achieving sustainable water management requires a reorientation emphasizing measurement infrastructure in data-poor contexts, North–South partnerships, and the integration of socioinstitutional dimensions as constitutive elements within technical development frameworks. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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29 pages, 1730 KB

Open AccessReview

Hydrogel Applications in Nitrogen and Phosphorus Compounds Recovery from Water and Wastewater: An Overview

by Daniel Szopa, Paulina Wróbel, Beata Anwajler and Anna Witek-Krowiak

Sustainability 2024, 16(15), 6321; https://doi.org/10.3390/su16156321 - 24 Jul 2024

Cited by 7 | Viewed by 4828

Abstract

This article provides an overview of the diverse applications of hydrogels in nutrient recovery from water and wastewater. Due to their unique properties, such as high water-retention capacity, nutrient rerelease, and tunable porosity, hydrogels have emerged as promising materials for efficient nutrient capture [...] Read more.

This article provides an overview of the diverse applications of hydrogels in nutrient recovery from water and wastewater. Due to their unique properties, such as high water-retention capacity, nutrient rerelease, and tunable porosity, hydrogels have emerged as promising materials for efficient nutrient capture and recycling. It has been suggested that hydrogels, depending on their composition, can be reused in agriculture, especially in drought-prone areas. Further research paths have been identified that could expand their application in these regions. However, the main focus of the article is to highlight the current gaps in understanding how hydrogels bind nitrogen and phosphorus compounds. The study underscores the need for research that specifically examines how different components of hydrogel matrices interact with each other and with recovered nutrients. Furthermore, it is essential to assess how various nutrient-recovery parameters, such as temperature, pH, and heavy metal content, interact with each other and with specific matrix compositions. This type of research is crucial for enhancing both the recovery efficiency and selectivity of these hydrogels, which are critical for advancing nutrient-recovery technologies and agricultural applications. A comprehensive research approach involves using structured research methodologies and optimization techniques to streamline studies and identify crucial relationships. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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Other

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40 pages, 670 KB

Open AccessSystematic Review

AI Solutions for Improving Sustainability in Water Resource Management

by Jorge Alejandro Silva

Sustainability 2026, 18(4), 2154; https://doi.org/10.3390/su18042154 - 23 Feb 2026

Viewed by 1315

Abstract

Water systems experience increasing sustainability challenges from climate variability, aging infrastructure, and energy and chemical intensity demands, but AI has typically been assessed against prediction accuracy rather than demonstrated operational success. This PRISMA 2020 systematic review analyzed the role of AI solutions on [...] Read more.

Water systems experience increasing sustainability challenges from climate variability, aging infrastructure, and energy and chemical intensity demands, but AI has typically been assessed against prediction accuracy rather than demonstrated operational success. This PRISMA 2020 systematic review analyzed the role of AI solutions on sustainability in distribution, treatment, and basin management. The database search identified 920 records; after deduplication (n = 185), screening was conducted on n = 735 titles/abstracts and examination of the full text for n = 85, providing a total of n = 41 included peer-reviewed studies for qualitative synthesis and n = 38 for quantitative/bibliometric synthesis with the additional analysis of seven grey-literature sources. Evidence mapping reveals high growth post-2020, and distribution and wastewater operations are dominated by a few companies. The most deployable evidence is found with monitoring, anomaly/leak detection, and short-term forecasting, while optimization and reinforcement-learning control are primarily simulation validated with limited field applications. While accuracy metrics are often reported, transformation into water saved, kWh/m³, chemicals, compliance/reliability/resilience/equity measures are inconsistently and less frequently operationalized. In general, AI is most believable when it is part of analysis-ready workflows, bounded decision support, and measurement-and-verification. Full article

(This article belongs to the Special Issue AI Solutions for Improving Sustainability in Water Resource Management)

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