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Applications of Artificial Intelligence (AI) in Water Resources System, 2nd Edition

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1470

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Special Issue Editor

Prof. Dr. Avi Ostfeld

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Guest Editor

Faculty of Civil and Environmental Engineering, Technion, Haifa 32000, Israel
Interests: water distribution systems analysis; optimal control; equity; robust optimization; multi-criteria decision-making; evolutionary algorithms
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Special Issue Information

Dear Colleagues,

Artificial Intelligence has revolutionized 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 System, 2nd Edition”, machine learning algorithms will be explored with a focus on 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 the field of water resource management, addressing challenges from urban planning to agricultural efficiency and beyond.

Prof. Dr. Avi Ostfeld
Guest Editor

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Keywords

water resources systems
artificial intelligence
modeling
optimization
water distribution systems

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Applications of Artificial Intelligence (AI) in Water Resources Systems in Water (9 articles)

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Research

42 pages, 10246 KB

Open AccessArticle

Enhancing Karst Spring Discharge Simulation Through a Hybrid XGBoost–BiLSTM Machine Learning Framework

by Mohamed Hamdy Eid, Attila Kovács and Péter Szűcs

Water 2026, 18(9), 1038; https://doi.org/10.3390/w18091038 - 27 Apr 2026

Viewed by 688

Abstract

Accurate simulation of karst spring discharge is critical for sustainable water resource management, yet it remains a significant challenge due to the inherent complexity, heterogeneity, and non-linearity of karst systems. While machine learning models have been increasingly applied to this problem, standalone algorithms often struggle to simultaneously capture complex temporal dependencies and maintain robust generalization. This study provides a comprehensive comparative assessment of five state-of-the-art machine learning (ML) models for forecasting the daily discharge of the Jósva Spring, located in the World Heritage Aggtelek karst area. The main goal of the study is to determine which modern machine learning approach can most accurately forecast the daily discharge of the Jósva Spring using meteorological data and the discharge of a hydraulically connected upstream spring. This is motivated by the need for a reliable operational prediction tool for complex karst aquifers, the improved water-resource management in a climate-sensitive region, and a lack of comparative studies evaluating multiple ML paradigms on the same karst system. The study also aimed at comparing the predictive performance of five state-of-the-art ML models to identify the most accurate and robust model and to understand the predictability of the karst system by analyzing feature importance, lag effects, and temporal dependencies. Three tree-based ensemble models (Random Forest, XGBoost, and Extra Trees) and two deep learning architectures (a Bidirectional Long Short-Term Memory network, BiLSTM, and a novel Hybrid XGBoost–BiLSTM model) were trained using a five-year (2015–2019) daily dataset comprising rainfall, temperature, and upstream discharge. The modeling framework was designed for synchronous simulation (lead time = 0 days), estimating concurrent downstream discharge using upstream and meteorological measurements from the same time step. A rigorous feature-engineering workflow was implemented based on statistical characterization, correlation analysis, and time-series diagnostics. Models were trained on 80% of the dataset and evaluated on an independent 20% test set. The results demonstrate that the proposed Hybrid XGBoost-BiLSTM model achieved the highest predictive accuracy on the unseen test data (R² = 0.74, NSE = 0.74, RMSE = 716.35 L/min). While the standalone tree-based models, particularly XGBoost (R² = 0.66), also exhibited strong and competitive performance, the hybrid architecture provided a consistent and measurable improvement across all evaluation metrics. The hybrid model’s success is attributed to its synergistic design, which leverages the powerful feature extraction and refinement capabilities of XGBoost to provide a more informative input space for the BiLSTM, thereby enhancing its ability to capture complex temporal dependencies while mitigating overfitting. Feature importance analysis confirmed that upstream discharge at a 3-day lag was the most critical predictor, highlighting the system’s hydraulic connectivity. This research provides clear, evidence-based guidance showing that hybrid machine learning architectures, which integrate the strengths of different modeling paradigms, represent the most effective approach for developing robust and reliable operational prediction tools for complex karst aquifers. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence (AI) in Water Resources System, 2nd Edition)

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22 pages, 11111 KB

Open AccessArticle

DeePC Sensitivity for Pressure Control with Pressure-Reducing Valves (PRVs) in Water Networks

by Jason Davda and Avi Ostfeld

Water 2026, 18(2), 253; https://doi.org/10.3390/w18020253 - 17 Jan 2026

Viewed by 433

Abstract

This study provides a practice-oriented sensitivity analysis of DeePC for pressure management in water distribution systems. Two public benchmark systems were used, Fossolo (simpler) and Modena (more complex). Each run fixed a monitored node and pressure reference, applied the same randomized identification phase followed by closed-loop control, and quantified performance by the mean absolute error (MAE) of the node pressure relative to the reference value. To better characterize closed-loop behavior beyond MAE, we additionally report (i) the maximum deviation from the reference over the control window and (ii) a valve actuation effort metric, normalized to enable fair comparison across different numbers of valves and, where relevant, different control update rates. Motivated by the need for practical guidance on how hydraulic boundary conditions and algorithmic choices shape DeePC performance in complex water networks, we examined four factors: (1) placement of an additional internal PRV, supplementing the reservoir-outlet PRVs; (2) the control time step

(Δ t)

; (3) a uniform reservoir-head offset

(Δ h)

; and (4) DeePC regularization weights

{(λ}_{g}, λ_{u}, λ_{y})

. Results show strong location sensitivity, in Fossolo, topologically closer placements tended to lower MAE, with exceptions; the baseline MAE with only the inlet PRV was 3.35 [m], defined as a DeePC run with no additions, no extra valve, and no changes to reservoir head, time step, or regularization weights. Several added-valve locations improved the MAE (i.e., reduced it) below this level, whereas poor choices increased the error up to ~8.5 [m]. In Modena, 54 candidate pipes were tested, the baseline MAE was 2.19 [m], and the best candidate (Pipe 312) achieved 2.02 [m], while pipes adjacent to the monitored node did not outperform the baseline. Decreasing

Δ t

across nine tested values consistently reduced MAE, with an approximately linear trend over the tested range, maximum deviation was unchanged (7.8 [m]) across all

Δ t

cases, and actuation effort decreased with shorter steps after normalization. Changing reservoir head had a pronounced effect: positive offsets improved tracking toward a floor of ≈0.49 [m] around

Δ h

≈ +30 [m], whereas negative offsets (below the reference) degraded performance. Tuning of regularization weights produced a modest spread (≈0.1 [m]) relative to other factors, and the best tested combination (λy, λg, λu) = (10², 10⁻³, 10⁻²) yielded MAE ≈ 2.11 [m], while actuation effort was more sensitive to the regularization choice than MAE/max deviation. We conclude that baseline system calibration, especially reservoir heads, is essential before running DeePC to avoid biased or artificially bounded outcomes, and that for large systems an external optimization (e.g., a genetic-algorithm search) is advisable to identify beneficial PRV locations. Full article