Articles
Publication Date: 2026
Reliability Engineering and System Safety (0951-8320)273
To address the limitations of conventional Graph Theory ( GT ) techniques, in this study, an effective approach, named the Graph Theory-based Pressure and Chlorine Quantities (GT-PCQ) method, is proposed inspired by Girvan–Newman ( GN ) algorithm. Here, the nodal pressure and chlorine concentration values are directly integrated into the network partitioning process. Three performance indices—Hydraulic Reliability Index ( HRI ), Quality Reliability Index ( QRI ), and Hydraulic–Quality Reliability Index ( HQRI )—are employed to evaluate the performance of the resulting configurations. The GT-PCQ method is applied to a large-scale, real-world Water Distribution System (WDS) in Najaf Abad, Isfahan, Iran, using a 24-hour Extended Period Simulation (EPS) combined with Pressure-Dependent Analysis (PDA) modeling for both hot and cold day scenarios. Three models are developed to assess the influence of system quantities, including (i) simultaneous consideration of pressure and chlorine concentration, (ii) pressure-only, and (iii) chlorine-only. Results indicate that, compared to the GN algorithm, the GT-PCQ method substantially reduces computational time ( CT ) and improves average network pressure ( P̅ ) and chlorine concentration (CL‾), ultimately leading to improved reliability across all indices. © 2026 Elsevier Ltd.
Publication Date: 2026
Water Resources Management (09204741)40(3)
In this research, a new approach is proposed to determine an optimal discrete and continuous hedging rule (HR) for the Marun Dam reservoir in Iran, considering climate change conditions. In the proposed method, hydrological modeling, optimization approaches, and climate projections are combined to determine the reservoir operation policies under drought conditions. In the optimization approaches, the set of HR coefficient variables and starting and ending storage volumes for the discrete HR are determined using a genetic algorithm (GA). Here, climate change is simulated using the CanESM2 model under the RCP 8.5 scenario. In addition, artificial neural network (ANN) and genetic programming (GP) models are applied to predict reservoir inflow values. The performance of the proposed approaches is evaluated using reliability (time and volume), vulnerability, and sustainability indices. Results show that discrete HR leads to higher reservoir storage value at the end of the operation period (655 MCM) compared with standard operation policy (SOP) (285.48 MCM) and continuous HR (469.12 MCM). Furthermore, the discrete HR also reduces the maximum monthly shortage volume, from 424 to 338 MCM. Nevertheless, when compared to SOP, its performance is inferior in certain indices, including maximizing reservoir storage and minimizing severe shortages. This study demonstrates that discrete HR offers a more robust strategy for reservoir management under climate change in which it is improved by considering drought condition in modeling. It provides valuable insights for water resources planning and policy-making. © The Author(s), under exclusive licence to Springer Nature B.V. 2026.
Publication Date: 2025
Stochastic Environmental Research And Risk Assessment (14363259)39(4)pp. 1605-1621
One major characteristic of the Standardized Drought Indices is their dependence on one variable. However, different variables influence drought simultaneously. To address this issue, this study modifies and develops the Surface Water Supply Index (SWSI) to involve multiple aspects of droughts. Data on precipitation, runoff, reservoir volume, and discharge (instead of snow) over 21 years are used to modify the SWSI in Marun, Khuzestan Province, Iran. Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), Standardized Reservoir Storage Index (SRSI), and Standardized Discharge Index (SDI) are used as standard univariate indices to evaluate a new comprehensive index. The Shannon Entropy (SE) method is utilized innovatively to determine the weights of the comprehensive index, instead of deriving them depending on the weather experts. Additionally, a Modified-Surface Water Supply Index (M-SWSI) is also proposed to align it with the existing standard indices. For comparison, the weights are also calculated using the Proportioning Objective Procedure (POP) method, and presented in the POP-SWSI index. The results indicate that utilizing the SE method to determine weights provides an exceptional perspective on the meteorological and hydrological conditions of the drought-affected upstream region; while determining the weights using the POP method provides an insightful socio-economic and hydrological view on the drought-affected downstream region. In 2017, one of the most severe years, the M-SWSI index detected a drought event one month earlier than the POP-SWSI, with a drought duration value of 6 months close to the duration value of 5 months identified by POP-SWSI. Furthermore, the severity values were similar between the two indices, although the M-SWSI indicated lower drought severity than the univariate indices. Copulas are also employed for drought events analysis and to build the joint distribution function of drought severity (S) and duration (D) for the M-SWSI. Bivariate cumulative probability distribution functions are created and analyzed to determine the periodic “and” and “or” bivariate drought return periods. Additionally, Severity-Duration-Frequency (SDF) curves are established to evaluate the M-SWSI index. The Marun dam has been chosen as a case study to analyze the surface water supply under drought conditions, aiming to develop management policies for downstream decision-making. Consequently, the M-SWSI can be applied to any dam reservoir for similar analyses. This index involves various aspects of drought and can be utilized for reservoir management, assessing risks, and addressing flood and flood risk management challenges. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Publication Date: 2025
Results in Engineering (25901230)25
Completing gaps in groundwater level (GWL) data is critical for reliable hydrological analysis and modeling. The irregularities and missing values often present in GWL measurements necessitate robust methods for infilling. In this study, an innovative approach is proposed to reconstruct the GWL time series by addressing data gaps. Historical GWL data spanning 20 years from 40 observation wells in the Lenjanat aquifer, Isfahan Province, Iran, are used. Three clustering methods—K-means, Fuzzy C-means (FCM), and Self-Organizing Map (SOM)—are applied to group the wells, and a novel metric, the Correlation-based Reliability Index (CRI), is introduced to identify the most suitable clustering method for GWL prediction. The selected cluster is analyzed using four machine learning and hybrid models, including support vector regression (SVR) with linear, polynomial, and radial basis function (RBF) kernels, genetic programming (GP), artificial neural networks (ANN), and a hybrid model combining SVR with the whale optimization algorithm (WOA) (WOA-SVR). Results reveals that the SVR model with an RBF kernel outperformed its counterparts with linear and polynomial kernels, while the hybrid WOA-SVR model shows superior performance compared to other models. The hybrid approach significantly improves the accuracy of GWL gap completion. The WOA-SVR model leads to average R2, RMSE, MAPE, and NS values of 0.81 (0.74), 0.55 m (0.95 m), 0.02 % (0.04 %), and 0.80 (0.72) for the training (test and validation) data, respectively. The results demonstrates that the proposed approach, integrating clustering, CRI, and machine learning models, can effectively fill GWL gaps and provide clean data for further hydrological modeling and groundwater management. © 2025 The Authors
