Articles
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.
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
Environment, Development and Sustainability (1387585X)
In this study, a novel approach is proposed in which the transition probability matrix (TPM) of stochastic dynamic programming (SDP) methods is determined based on the copula function (TPM-C) by calculating the conditional probability. The obtained results are compared with the TPM based on the Markov Chain method (TPM-M). Here, the Marun dam reservoir, in southwestern Iran, is selected as a case study. In addition, for the historical period, the best values of reliability, resilience, vulnerability, sustainability, a minimum of water deficit as an objective function are obtained, and the average reservoir storage value of TPM-C is better than TPM-M. Finally, to update the reservoir operation rule curves, three different scenarios are proposed depending on the historical and future conditions. For this purpose, the water inflow values into the dam reservoir are predicted using a Multi-Layer Perceptron (MLP) model depending on the predicted precipitation of the climate change model. Therefore, the inflows, reservoir storage volumes, and water demand values are used to simulate and forecast the water release values into the reservoir. The results show the superior performance of the proposed TPM-C compared to the TPM-M approach to determine the reservoir operation rule curves at uncertain future conditions. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.
Journal of Water Resources Planning and Management - ASCE (07339496)151(10)
Graph theory-based methods are widely used to partition water distribution networks (WDNs) into district metered areas (DMAs). In general, the Girvan-Newman (GN) algorithm partitions networks based solely on the edge betweenness, a purely topological criterion, without considering the intrinsic hydraulic characteristics of WDNs. To address this limitation, in this study, an improved approach, named the improved Girvan-Newman (IGN) algorithm, is proposed, which integrates hydraulic parameters into the partitioning process. Specifically, pressure (as a quantity parameter) and chlorine concentration (as a quality parameter) are incorporated into the algorithm. The IGN method involves modeling the WDN as a graph, simulating it using either the demand-driven simulation method or the head-driven simulation method during a 24-h extended period simulation (EPS), and assigning nodal weights based on hydraulic values. Edge scores are then calculated using these nodal weights, and the edge with the lowest score is iteratively removed, with corresponding links closed in the hydraulic model. Then, the WDN is reanalyzed to update the hydraulic parameters. This iterative process continues until the desired number of DMAs is achieved. Two case studies, a small-scale WDN, named Poulakis, and a large-scale real WDN of Baharestan city in Isfahan, Iran, are used to investigate the performance of the IGN algorithm. In Poulakis WDN, by using IGN, the average nodal pressure decreases by 1.6%, the chlorine concentration increases by 9.1%, and the hybrid reliability improves by 3.7% compared to the standard GN algorithm. In addition, in Baharestan WDN, the related values are 5.1%, 3.4%, and 4.2%, respectively. The IGN method also enables flexible and time-dependent DMA configurations during EPS, in which the final configuration is selected based on a floating index. Here, a novel integration of hydraulic features into the GN algorithm is proposed, offering a more effective and reliable approach to WDN partitioning and management. © 2025 American Society of Civil Engineers.
