Applied Water Science最新文献

{"title":"A study on the GRU-LSTM hybrid model with dual attention mechanism for reservoir water level forecasting","authors":"Guangying Yang,&nbsp;Fan Zhou","doi":"10.1007/s13201-026-02893-z","DOIUrl":"10.1007/s13201-026-02893-z","url":null,"abstract":"<div><p>Reservoir water level forecasting is essential for flood control and water resource optimization, yet remains challenging due to the nonlinear, non-stationary, and multi-scale nature of hydrological processes. This paper proposes a hybrid deep learning model (GLA-GRU-LSTM) that integrates GRU and LSTM with dual attention mechanisms to capture both long-term trends and short-term fluctuations in water level dynamics. The global attention module extracts long-range dependencies across the entire sequence, while the local attention module focuses on abrupt changes within a recent temporal window. The model was evaluated using ten-year (2014–2023) hydrometeorological data from Yefan Reservoir, China, and compared against SVR, LSTM, CNN-LSTM, Transformer, TFT, and RFM_Trans. For 7 day forecasting, GLA-GRU-LSTM achieved NSE of 0.934 and RMSE of 1.647, outperforming Transformer (NSE = 0.921) and TFT (NSE = 0.916). For 30 day forecasting, it maintained superior performance (NSE = 0.807, RMSE = 2.773), with the performance gap widening as forecast horizon extended. Ablation studies confirm that performance gains stem from synergistic integration of GRU-LSTM hybridization and dual attention, not merely increased parameterization. Statistical significance tests (Diebold-Mariano, p &lt; 0.05) and bootstrap confidence intervals confirm the robustness of improvements. The proposed framework provides an accurate and interpretable approach for multi-scale reservoir water level forecasting, with potential applications in operational water management.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02893-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic implications of non-revenue water and long-run marginal cost for Tulcán’s public water utility","authors":"David Carrera-Villacrés,&nbsp;Fabián Rodríguez-Espinosa","doi":"10.1007/s13201-026-02869-z","DOIUrl":"10.1007/s13201-026-02869-z","url":null,"abstract":"<div><p>Non-revenue water (NRW) is a persistent global problem generated in municipal systems, causing millions of cubic meters of water to be wasted per year, becoming a financial obstacle for water and sanitation companies, and generating economic losses. The main objective of this study is to determine the NRW in the supply system of the Tulcán canton from information provided by the Empresa Pública Municipal de Agua Potable y Alcantarillado de Tulcán (EPMAPA-T) during the period 2013 to 2020, where the NRW, the Physical Performance Indicator (PPI), which measures the technical and operational efficiency of the water distribution system, and the Return Flow Coefficient, a fraction of the produced water that returns to the sewer system, were analyzed. The estimation of Non-Revenue Water (NRW) and the Physical Performance Indicator (PPI) was based on the comparison of volumetric records, including billed drinking water, water output from the Water Treatment Plant (WTP), and influent to the Sewage Treatment Plants (STPs). They are in a range of 37% to 43% and 57% to 63%, respectively, and with a return coefficient of 0.93. In conclusion, an average NRW of 41% was presented, indicating that not all the produced water enters the sewer system, reflecting a total loss of $ 509,802.69 during the period from 2013 to 2020. In Ecuador, studies on NRW are still incipient but strategic; this work has shown that almost half of the drinking water produced does not generate income, highlighting the magnitude of its losses and cost.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02869-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting of daily reference evapotranspiration using random tree, ANFIS and multi-regression methods","authors":"Bestami Taşar","doi":"10.1007/s13201-026-02877-z","DOIUrl":"10.1007/s13201-026-02877-z","url":null,"abstract":"<div><p>Evapotranspiration plays a critical role in water resource management and reservoir operation; however, accurately estimating it remains challenging, particularly when meteorological data are variable. Although machine learning techniques have been widely applied in evapotranspiration modeling, the systematic influence of progressively increasing combinations of meteorological inputs on the performance of hybrid and regression-based models has not been comprehensively evaluated. In this study, reference evapotranspiration (ET) was calculated using the FAO-56 Penman–Monteith method and used as the benchmark variable. Daily meteorological data, including solar radiation (SR), air temperature (T), relative humidity (RH), and wind speed (WS), were obtained from a station located at Lake Hartwell, South Carolina (USA). Four structured input combinations (SR; SR + T; SR + T+RH; SR + T+RH + WS) were developed to assess the incremental contribution of meteorological variables. The predictive performance of Multiple Linear Regression (MLR), Random Tree (RT), Adaptive Neuro-Fuzzy Inference System (ANFIS), and three polynomial regression variants (Q-MR, PQ-MR, and I-MR) was evaluated and compared with empirical Hargreaves–Samani (HS) and Turc methods using RMSE, MAE, APE, and the coefficient of determination (DC). Results demonstrated a consistent improvement in model accuracy with increasing input dimensionality. In the four-input configuration, Q-MR4 and I-MR4 achieved the highest predictive performance (RMSE = 0.073–0.074 mm/day; DC = 0.998), followed by ANFIS4 (RMSE = 0.121 mm/day; DC = 0.991). In contrast, empirical methods showed substantially higher error levels (RMSE up to 1.549 mm/day) and reduced agreement, particularly during peak evaporative conditions. Overall, the findings indicate that incorporating non-linear and interaction terms within regression frameworks provides a computationally efficient yet highly accurate alternative to more complex hybrid models for ET₀ estimation in reservoir-based climatic settings.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02877-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photodegradation of azo dyes by advanced oxidation process using magnetic composites including titania supported by graphene oxide: a statistical analysis study","authors":"Sedigheh Abbasi","doi":"10.1007/s13201-026-02888-w","DOIUrl":"10.1007/s13201-026-02888-w","url":null,"abstract":"<div><p>Advanced oxidation process using magnetic graphene oxide decorated with variable content of titania nanoparticles (labeled as MGO@TiO₂#1 and MGO@TiO₂#2) is used as an effective technique for photo disintegration of methyl orange as an azo dye from industrial wastewater. The efficacy of time, pH, and interaction of them on disappearance rate is reported. The results found show that the rate of degradation is significantly affected by the time and increases significantly with it enhance. The dependence of the rate of methyl orange degradation on pH indicates a change in the trend with increasing pH. As the pH enhances from the acidic to neutral zone (from pH = 3 to pH = 7), the decomposition rate decreases, and after increasing to the alkaline zone (pH = 11), an enhancing trend is achieved. Such a behavioral change is related to the difference in the number of free ions in acidic and alkaline suspensions. Comparing the effect of the two synthesized photocatalysts on the rate of breaking of azo dye bonds indicates that MGO@TiO₂#2 has greater power in increasing degradation, which can be attributed to its higher content of photocatalytic nanoparticles compared to MGO@TiO₂#1. Statistical studies of the results using analysis of variance confirm the significant effect of experimental parameters on the rate of degradation. Therefore, statistical models consist of only individual parameters and their interactivity that have a notable efficacy. The proposed models are capable of estimating alterations in the yield of degradation with regard to effective factors at a 95% confidence level.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02888-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance and regeneration of a sustainable composite adsorbent in fixed-bed columns for landfill leachate treatment","authors":"Amir Detho,&nbsp;Mohd Arif Rosli,&nbsp;Zawawi Daud,&nbsp;Ahmed Othman Alanazi,&nbsp;Aeslina Abdul Kadir,&nbsp;Asif Ali Memon,&nbsp;Urooj Gul Sandilo,&nbsp;Hesham Hussein Rassem","doi":"10.1007/s13201-026-02859-1","DOIUrl":"10.1007/s13201-026-02859-1","url":null,"abstract":"<div><p>The present study was primarily designed to develop a novel composite material composed of peat, limestone, zeolite, and activated carbon (PS-LS-ZE-AC) and to evaluate its simultaneous effectiveness in removing ammoniacal nitrogen (NH₃-N) and chemical oxygen demand (COD) from stabilised landfill leachate. Batch-scale adsorption analyses were performed to identify the optimum compositional ratios, with PS-AC were classified as hydrophobic adsorbents, with the optimum ratio identified as 1.5:2.5. LS-ZE were classified as hydrophilic adsorbents, with the optimum ratio determined as 15:25. The optimal proportion of hydrophobic to hydrophilic adsorbents was determined to be 4:4, as this ratio demonstrated superior adsorption performance for NH₃-N and COD. Characterization of composite was carried out using XRF, FTIR, and SEM. XRF analysis revealed that the main constituents were present in high concentrations of CaO and SiO₂. The composite adsorbents contained several dominant functional groups, notably O–C, C–N, Si–O–Si, O–H, N–H, and C–O. SEM testing demonstrated rough and heterogeneous porous surface of the composite adsorbent. The effects of shaking speed and dosage were examined in conjunction with the reduction of NH₃-N and COD. Fixed-bed columns (FBCs) adsorption experiments indicated reduction efficacy of NH₃-N and COD achieving 99% and 98%. An increase in flowrate resulted in shorter breakthrough and saturation times. The experimental results showed significantly more correlation with the Thomas and Yoon-Nelson compared to Adams-Bohart model. The composite adsorbent was reusable following regeneration with 0.5 M NaCl solution at pH 12, and adjusting using 1 M NaOH, achieving adsorption capacities of NH₃-N and COD 34.13 mg/g and 33.22 mg/g. These findings highlight the efficacy of PS-LS-ZE-AC-based composite adsorbents in treating stabilized landfill leachate.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02859-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and optimization of an integrated magnetohydrodynamic–magnetophoretic electrochemical reactor for low-energy deionization from brackish water","authors":"Maziar Naderi,&nbsp;Amir Hossein Mahvi,&nbsp;Kazem Naddafi,&nbsp;Alireza Mesdaghinia,&nbsp;Mahmood Alimohammadi","doi":"10.1007/s13201-026-02894-y","DOIUrl":"10.1007/s13201-026-02894-y","url":null,"abstract":"<div><p>Electrochemical water treatment technologies are increasingly investigated as sustainable and chemical-free alternatives for low-salinity desalination and water softening. However, many systems remain energy intensive or insufficiently optimized. This study revealed the modeling, experimental evaluation, and optimization of an integrated electrochemical–magnetic reactor and integrated magnetohydrodynamic (MHD, Lorentz force) and magnetophoretic (MP, Kelvin force) effects for ion removal from brackish water. The aims were to (i) model the coupled electromagnetic forces, (ii) optimize operational parameters using Central Composite Design (CCD) and Response Surface Methodology (RSM), and (iii) evaluate both removal efficiency and specific energy consumption. A total of 181 experimental runs were conducted by varying salt concentration (≤ 2.5 g/L NaCl equivalent), flow rate, electrode spacing, pipe diameter, and magnetic flux density generated by a 1500-turn coil. The removal efficiency of total dissolved solids (TDS), electrical conductivity (EC), and total hardness (TH) was also evaluated. The control experiments were performed to decouple MHD-only and MP-only contributions. The results demonstrated that the Lorentz force significantly enhanced the ion removal through induced ionic motion and spiral (Larmor-type) trajectories, whereas the Kelvin force alone indicated the ion redistribution and localized concentration gradients. Moreover, the highest performance occurred when both forces acted simultaneously. Under the optimized conditions, the system achieved 64.7% TDS removal, 32.38% EC reduction, and 36.04% hardness removal, with a reactor-level specific energy consumption of 0.0416 kWh/m³. While this removal efficiency is insufficient for single-pass potable standards, the results provided strong potential as a low-energy pre-treatment or multi-stage (cascade) polishing process capable of achieving &gt; 95% removal. Furthermore, MgSO₄ revealed significant magnetic reactivity that was attributed to paramagnetic contributions associated with oxygen-containing species. Although the experiments were conducted using synthetic brackish solutions, the findings provide a mechanistic and statistically optimized framework for magnetic–electrochemical ion separation. Future work should focus on multi-stage reactor integration, validation with real saline waters containing total suspended solids (TSS), organic matter and turbidity, and the evaluation of scale-up energy requirements such as hydraulic and power system losses.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02894-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting drought characteristics under CMIP6 climate change scenarios in southern Kerman Province","authors":"Saghi Neshat,&nbsp;Baharak Motamedvaziri,&nbsp;Hadi Kiadaliri,&nbsp;Mahdi Sarai Tabrizi","doi":"10.1007/s13201-026-02895-x","DOIUrl":"10.1007/s13201-026-02895-x","url":null,"abstract":"<div><p>Climate change is now recognized as one of the most important environmental challenges of the 21st century. Global warming, coupled with a decrease in precipitation, has exacerbated the effects of climate change. Assessing drought severity and distribution within a region and devising strategies to mitigate its impacts constitutes a central component of drought management. Recently, General Circulation Models (GCMs) have been implemented to predict the impacts of climate change (CC) on various systems, including water resources and others. Therefore, this study examines the effects of CC on temperature and precipitation parameters using CMIP6 climate models in southern Kerman Province. The study implements data from the CanESM5 predictor model and the SDSM downscaling model, based on emission scenarios developed for the baseline period (1998–2022), to forecast future atmospheric conditions. The Standardized Precipitation Index (SPI) was used for monitoring temporal and spatial patterns of drought variations under present and future conditions. The SPI was calculated using precipitation data from 24 meteorological stations across time scales of 1, 3, 6, 9, 12, 18, 24, and 48 months during the 1998–2022 statistical period. Climate variables were downscaled and simulated using the Coupled Model Intercomparison Project Phase 6 (CMIP6) framework by applying the Shared Socioeconomic Pathways (SSPs; the optimistic SSP1-2.6 and pessimistic SSP5-8.5) to project a near-future twenty-year period spanning 2040 to 2059. The performance of CMIP6 was evaluated by implementing two model evaluation metrics, as Root Mean Square Error (RMSE) and the coefficient of determination (R²). Temporal and spatial patterns of drought were evaluated for the historical baseline and future scenarios. Baseline time series analysis revealed that short-term droughts were more frequent but less persistent than long-term droughts. Spatial analysis indicated that most of the region experienced predominantly normal conditions, with wetter conditions concentrated in the northern and northwestern areas, while southern regions showed higher susceptibility to drought. Rainfall simulations using the SDSM model during the baseline period demonstrated high reliability, with <i>R²</i> values varying from 0.63 to 0.98 across different meteorological stations. Future projections suggest prolonged drought episodes, with drought conditions expected in 42–58% of cases across 3 to 24-month time scales under both scenarios. Southern and southwestern parts of the region are identified as areas with the highest likelihood of future drought occurrence. The results offer critical resources to better manage agriculture and water in the area.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02895-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coupling computational fluid dynamics with Decision Tree and k-Nearest Neighbours classifications for stagnation and water quality assessment in arid rivers: evidence from the Tigris","authors":"Mustafa Hathal,&nbsp;Viktor Sebestyén,&nbsp;Stanisław Anweiler,&nbsp;Viola Somogyi","doi":"10.1007/s13201-026-02883-1","DOIUrl":"10.1007/s13201-026-02883-1","url":null,"abstract":"<div><p>Urban river systems in arid climates are experiencing extreme hydrological shifts driven by evolving drought patterns and upstream flow regulation. Despite these transformations, the precise physical mechanisms linking reach-scale stagnation to water quality deterioration remain inadequately resolved. In this study, a Comprehensive Water Assessment Framework was developed by integrating k–ε turbulence-based Computational Fluid Dynamics (CFD) with interpretable machine learning (ML) classifiers—Decision Tree and k-Nearest Neighbour. This framework was applied to diagnose flow dynamics in the Tigris River, Baghdad, over the period 2014–2023. It was revealed that flow stagnation responds non-linearly to discharge, with the stagnant fraction expanding to ~ 72% of the reach length during extreme drought, coinciding with critical Water Quality Index (WQI) collapses (51–55). An empirically derived safe flow threshold of ≈ 200 m³/s was identified, below which hydraulic connectivity and self-purification capacity dropped sharply (R² = 0.98). Furthermore, WQI variability was shown to be strongly governed by a minimal set of drivers, including stagnation extent, turbidity, pH, TDS, Ca²⁺, and PO₄³⁻with a dominant stagnation × turbidity interaction highlighted via multivariate response-surface analysis (R² = 0.97). As CFD-derived diagnostics were replicated with ML models at &lt; 2% error, a high-accuracy, computationally efficient tool for real-time management is provided. These findings offer a scalable outline for drought operations, allowing high-risk urban hotspots to be identified and abstraction strategies to be optimized in water-stressed environments.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02883-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The law of survival of phenomena and its application in the analysis of dynamic processes (Case study: methodology and modeling of infiltration hydrodynamics)","authors":"Shayan Shamohammadi,&nbsp;Mohammad Reza Yabaluei khamesluei,&nbsp;Bahar Shamohammadi","doi":"10.1007/s13201-025-02711-y","DOIUrl":"10.1007/s13201-025-02711-y","url":null,"abstract":"<div><p>In this study, we combined general relativity relations with soil hydrodynamics knowledge to create a new understanding of the concepts of infiltration hydrodynamics while changing existing methods and models. For this reason, in this study, the definition of \"phenomenon\" previously presented by the author was expanded, then the law of \"conservation of change of phenomenon\", \"law of phenomenontime\" and \"law of irreversibility of phenomenon\" were introduced as the foundations of new modeling. After that, the law of conservation of \"spacetime\" (whose more complex and relativistic form was previously presented by Einstein in relativity physics), together with the law of irreversibility of \"space\", were used to model infiltration hydrodynamics. The new model, unlike previous models (which are empirical), is based on the fundamental laws of physics. The \"new model\" together with the \"new infiltration test method\" was able to obtain important water and soil parameters such as available water threshold (AWT), crop capacity (FC) and plant available water (PAW) without the need for time-consuming and expensive experiments. Also, the final equation was presented in two spacetime and spacespace forms for cumulative infiltration and infiltration rate. Also, in this study, a new definition was presented under the title \"Available Plant Water Threshold (AWT)\" instead of PWP, in order to establish coordination between the definition of FC and AWT.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-025-02711-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable irrigation planning using meta-heuristic algorithms","authors":"Deepjyoti Deb,&nbsp;Sankati Sai Vignan Reddy,&nbsp;Kesava Chodavarapu,&nbsp;Vasan Arunachalam,&nbsp;K. Srinivasa Raju","doi":"10.1007/s13201-026-02887-x","DOIUrl":"10.1007/s13201-026-02887-x","url":null,"abstract":"<div><p>The present study proposes a novel, systematic, multi-algorithm, benchmark-based screening framework. Ten Meta-Heuristic Algorithms (MHAs), namely, Sand Cat Swarm Algorithm (SCSA), Chaotic Sand Cat Swarm Algorithm (CSCSA), White Shark Algorithm (WSA), Grey Wolf Optimization (GWO), Gravitational Search Algorithm (GSA), Jaya Algorithm (JA), Chaotic Jaya Algorithm (CJO), Archimedes Optimization (AO), Teaching Learning Based Optimization (TLBO), Elephant Herding Optimization (EHO) covering four distinct inspiration categories (human, animal, physics, and mathematics) are evaluated on thirteen standard benchmark functions using a weighted ranking approach. TLBO, AO, CJO, and EHO are identified as the most promising algorithms after screening. Subsequently, these are used to optimize net annual benefits for the study area of the Sri Ram Sagar Project (SRSP), India. Additionally, the study explicitly models the conjunctive use of surface and groundwater under three scenarios, S1-S3: S1 and S2 represent 75% and 90% inflow dependability levels with groundwater utilization, respectively, while S3 is for a 75% inflow level without groundwater. The study also integrates environmental flow requirements, command-area limitations, groundwater withdrawals, canal capacity restrictions, and others. TLBO performed best, with optimal net annual benefits of ₹3815.08 crores for S1, ₹1766.83 crores for S2, and ₹2731.78 crores for S3, and respective irrigation intensities of 80.85%, 30.04%, 64.48%, indicating improvement in land and water-use efficiency. The structured screening methodology, which selects the best-performing algorithms across diverse categories, is the first of its kind at the global and national levels in irrigation planning. This approach also facilitates an adaptable framework for any large-scale reservoir systems worldwide.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 7","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02887-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}