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Applications of Artificial Intelligence (AI) in Water Resources Systems
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Applications of Artificial Intelligence (AI) in Water Resources Systems

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Urban Water Management".

Deadline for manuscript submissions: 25 October 2025 | Viewed by 8972

Special Issue Editor

Prof. Dr. Avi Ostfeld

E-Mail Website
Guest Editor
Faculty of Civil and Environmental Engineering, Technion, Haifa 32000, Israel
Interests: simulation; modeling; water resources systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence revolutionizes water resource management by enhancing predictive capabilities, optimizing usage, and improving decision-making processes. In this Special Issue, titled “Applications of Artificial Intelligence (AI) in Water Resources Systems”, machine learning algorithms will be explored in their ability to forecast water demand, detect anomalies in water resource system performance, and model hydrological cycles with unprecedented accuracy. AI-driven tools enable real-time monitoring and adaptive management of water systems, facilitating sustainable practices and resilience against climate variability. This collection highlights innovative applications demonstrating AI’s potential to transform water resource management, addressing challenges from urban planning to agricultural efficiency and beyond.

Prof. Dr. Avi Ostfeld
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • water resources systems
  • artificial intelligence
  • modeling
  • optimization
  • water distribution systems

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Published Papers (4 papers)

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Research

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14 pages, 6805 KiB  
Article
Transient Flow Dynamics in Tesla Valve Configurations: Insights from Computational Fluid Dynamics Simulations
by Mohamad Zeidan, Márton Németh, Gopinathan R. Abhijith, Richárd Wéber and Avi Ostfeld
Water 2024, 16(23), 3492; https://doi.org/10.3390/w16233492 - 4 Dec 2024
Cited by 2 | Viewed by 1397
Abstract
This study investigates the transient flow dynamics and pressure interactions within Tesla valve configurations through comprehensive CFD simulations. Tesla valves offer efficient passive fluid control without the need for external power, making them favorable in various applications. Previous observations indicated that Tesla valves [...] Read more.
This study investigates the transient flow dynamics and pressure interactions within Tesla valve configurations through comprehensive CFD simulations. Tesla valves offer efficient passive fluid control without the need for external power, making them favorable in various applications. Previous observations indicated that Tesla valves effectively reduce the amplitude of pressure transients, prolonging their duration and distributing energy over an extended timeframe. While suggesting a potential role for Tesla valves as pressure dampers during transient events, the specific mechanisms behind this behavior remain unexplored. This research focuses on elucidating the internal dynamics of Tesla valves during transient events, aiming to unravel the processes responsible for the observed attenuation in pressure transients. This study reveals the emergence of “pressure pockets” within Tesla valves, deviating from conventional uniform pressure fronts. These pockets manifest as discrete chambers with varying lengths and volumes, contributing to the non-uniform propagation of pressure throughout the system. This investigation employs advanced CFD simulations as a crucial tool to unravel the governing dynamics of transient flow within Tesla valve configurations. By elucidating underlying fluid dynamics, this study lays the groundwork for future Tesla valve design optimization, holding potential implications for applications where the control of transient flow events is crucial. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence (AI) in Water Resources Systems)
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19 pages, 4830 KiB  
Article
Integrating Policy Instruments for Enhanced Urban Resilience: A Machine Learning and IoT-Based Approach to Flood Mitigation
by Lili Wang, Linlong Bian, Arturo S. Leon, Zeda Yin and Beichao Hu
Water 2024, 16(23), 3364; https://doi.org/10.3390/w16233364 - 23 Nov 2024
Viewed by 1801
Abstract
In the context of global urbanization, the interconnected architecture of economic, social, and administrative activities in modern cities cultivates a complex web of interdependencies. This intricacy amplifies the impacts of natural disasters such as urban flooding, presenting unprecedented challenges in risk management and [...] Read more.
In the context of global urbanization, the interconnected architecture of economic, social, and administrative activities in modern cities cultivates a complex web of interdependencies. This intricacy amplifies the impacts of natural disasters such as urban flooding, presenting unprecedented challenges in risk management and disaster responsiveness. To address these challenges, this study defines the concept of urban flood resilience and outlines its practical applications in flood risk management, proposing an integrated resilience governance framework. The framework systematically enhances urban flood management by combining structural flood mitigation methods with advanced technologies, including the Internet of Things (IoT) and non-structural decision-support tools powered by Machine Learning Algorithms (MLAs). This integrated approach aims to improve early flood warning systems, optimize urban infrastructure planning, and reduce flood-related risks. The case study of the Cypress Creek watershed validates the framework’s effectiveness under specific scenarios, achieving reductions of 25% in inundation area, 30% in peak flow, and 20% in total flood volume. These results not only demonstrate the framework’s efficacy in mitigating flood impacts but also provide empirical support for developing resilient urban governance models, highlighting the essential role of adaptive policy instruments in urban flood management. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence (AI) in Water Resources Systems)
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13 pages, 3260 KiB  
Article
Reconstruction of Surface Water Temperature in Lakes as a Source for Long-Term Analysis of Its Changes
by Mariusz Sojka and Mariusz Ptak
Water 2024, 16(23), 3347; https://doi.org/10.3390/w16233347 - 21 Nov 2024
Cited by 1 | Viewed by 861
Abstract
One of the key parameters of lakes is water temperature, which influences many physical and biochemical processes. In Poland, in situ temperature measurements are or have been conducted in only about 30 lakes, whereas there are over 3000 lakes with an area larger [...] Read more.
One of the key parameters of lakes is water temperature, which influences many physical and biochemical processes. In Poland, in situ temperature measurements are or have been conducted in only about 30 lakes, whereas there are over 3000 lakes with an area larger than 10 hectares. In many cases, the length of existing observation series is not always sufficient for long-term analysis. Using artificial neural networks of the multilayer perceptron network (MLP) type, the reconstruction of average monthly water temperatures was carried out for nine lakes located in northern Poland. During the validation stage of the reconstruction results, BIAS values were obtained in the range of −0.33 to 0.44 °C, the mean absolute error was 0.46 °C, and the root mean square error was 0.61 °C. The high quality of the reconstructed data allowed for an assessment of water temperature changes in the analyzed lakes from 1993 to 2022 using the Mann–Kendall and Sen tests. It was found that, on an annual basis, the water temperature increased by an average of 0.50 °C per decade, ranging from 0.36 °C per decade to 0.64 °C per decade for individual lakes. For specific months, the largest increase was observed in November, about 0.99 °C per decade, and the smallest in May, 0.07 °C per decade. The obtained results confirm previous studies in this field while adding new data from lakes, which are particularly significant for the western part of Poland—a region with a previously limited number of monitored lakes. According to the findings, the analyzed lakes have undergone significant warming over the past three decades, which is important information for water management authorities. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence (AI) in Water Resources Systems)
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Review

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32 pages, 3499 KiB  
Review
Unlocking the Potential of Artificial Intelligence for Sustainable Water Management Focusing Operational Applications
by Drisya Jayakumar, Adel Bouhoula and Waleed Khalil Al-Zubari
Water 2024, 16(22), 3328; https://doi.org/10.3390/w16223328 - 19 Nov 2024
Cited by 7 | Viewed by 4335
Abstract
Assessing diverse parameters like water quality, quantity, and occurrence of hydrological extremes and their management is crucial to perform efficient water resource management (WRM). A successful WRM strategy requires a three-pronged approach: monitoring historical data, predicting future trends, and taking controlling measures to [...] Read more.
Assessing diverse parameters like water quality, quantity, and occurrence of hydrological extremes and their management is crucial to perform efficient water resource management (WRM). A successful WRM strategy requires a three-pronged approach: monitoring historical data, predicting future trends, and taking controlling measures to manage risks and ensure sustainability. Artificial intelligence (AI) techniques leverage these diverse knowledge fields to a single theme. This review article focuses on the potential of AI in two specific management areas: water supply-side and demand-side measures. It includes the investigation of diverse AI applications in leak detection and infrastructure maintenance, demand forecasting and water supply optimization, water treatment and water desalination, water quality monitoring and pollution control, parameter calibration and optimization applications, flood and drought predictions, and decision support systems. Finally, an overview of the selection of the appropriate AI techniques is suggested. The nature of AI adoption in WRM investigated using the Gartner hype cycle curve indicated that the learning application has advanced to different stages of maturity, and big data future application has to reach the plateau of productivity. This review also delineates future potential pathways to expedite the integration of AI-driven solutions and harness their transformative capabilities for the protection of global water resources. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence (AI) in Water Resources Systems)
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