Applied Water Science最新文献

{"title":"Assessing stratification and hypoxia mitigation through sluice gate operation in a brackish lake: a case study of Saemangeum Lake, South Korea","authors":"Semin Kim","doi":"10.1007/s13201-026-02967-y","DOIUrl":"10.1007/s13201-026-02967-y","url":null,"abstract":"<div><p>Salinity-driven stratification and bottom-water hypoxia are persistent water quality problems in semi-enclosed estuarine lakes. This study assessed how sluice gate operations influence stratification and dissolved oxygen (DO) dynamics in Saemangeum Lake, South Korea, by integrating long-term monitoring data (2013–2022), high-resolution field observations, and three-dimensional Delft3D modeling. Water-column stability was evaluated using the potential energy anomaly (PEA) and Richardson number (<span>(:{R}_{i})</span>), and the spatial extent of hypoxia was quantified under different gate-operation regimes. Persistent stratification was indicated by log(PEA) ≥ 1, while <span>(:{R}_{i})</span> remained ≥ 0.25 and commonly exceeded 100 under weak resolved vertical shear associated with reduced tidal forcing behind the dike, consistent with limited oxygen transfer to the sub-halocline layer. Depending on the operation schedule, hypoxic area (DO &lt; 2 mg L⁻¹) ranged from 45 to 100 km², and hypoxia developed within 6–7 days when seawater exchange was suspended. Scenarios exchanging similar annual volumes of seawater produced opposite changes in hypoxic extent, and alternating operation matched the improvement obtained by raising the controlled water level while reducing annual inflow by 43% relative to the reference condition. These results indicate that gate-operation pattern is an important control on hypoxia and that inflow volume alone does not determine the response, providing a practical basis for water quality management in brackish lake systems.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 10","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02967-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933320","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":"Potential of treated wastewater reuse for irrigation under semi-arid conditions and various climate scenarios","authors":"Abdulrahman K. Soufi,&nbsp;Rabi H. Mohtar,&nbsp;Sandra F. Yanni","doi":"10.1007/s13201-026-02958-z","DOIUrl":"10.1007/s13201-026-02958-z","url":null,"abstract":"<div><p>Water resources in arid and semi-arid regions are increasingly stressed by population growth and climate change, leading to growing challenges for agricultural production. This study investigates the potential of reusing treated wastewater (TWW) as a supplementary irrigation source under current and future climate scenarios. A comprehensive framework integrating economic and environmental dimensions is developed and applied to the Ablah region in the Beqaa Valley, Lebanon, where an operational wastewater treatment plant and irrigation network exists. Crop water requirements were estimated for major crops under baseline and projected climate conditions. Two scenarios other than the current were considered: a moderate scenario with reduced precipitation and increased reference evapotranspiration, and an extreme scenario with more pronounced climatic changes. Results indicate a substantial increase in irrigation water deficits under future conditions. An improved agricultural water management scenario incorporating higher shares of TWW was developed and assessed. The findings show that increasing TWW reuse can significantly reduce groundwater (GW) abstraction, support supplemental irrigation for cereals, and improve the economic feasibility of selected irrigation practices. An economic analysis demonstrated that TWW is more cost-effective ($0.18/m³) than GW ($0.48/m³). Furthermore, supplementary irrigation using TWW is economically viable for crops such as wheat and grapes, whereas GW-based irrigation is not feasible for wheat. The study introduces a novel integrated assessment framework combining water balance modeling with economic evaluation, providing a practical decision-support tool for sustainable irrigation planning and facilitating the adoption of TWW reuse in water-scarce regions.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 10","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02958-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148927307","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":"Study on surface modification of HDPE waste plastics to make bio-carriers for nutrient-rich domestic wastewater treatment","authors":"Duc Trinh Xuan,&nbsp;Hong Anh Do,&nbsp;Cuong Le Manh,&nbsp;Dinh Pham Van,&nbsp;Uyen Pham My,&nbsp;Thuy Anh Tran,&nbsp;Huyen Dang Thi Thanh","doi":"10.1007/s13201-026-02929-4","DOIUrl":"10.1007/s13201-026-02929-4","url":null,"abstract":"<div><p>For several decades, the valorization of plastic waste has been a top concern for the scientific community. Informed by prior research, this work explores the viability of reusing bottle caps as bio-carriers after their surface modification. The High-Density Polyethylene (HDPE) bottle caps were modified using KMnO₄, H₂SO₄, HCl and NaH₂PO₄ solutions, then employed as bio-carriers in the fixed bed bioreactor for treating nutrient rich domestic wastewater (NH₄⁺_in= 50 mg/L − 162 mg/L; COD_in = 490 mg/L − 570 mg/L). The initial results revealed that the removal efficiencies for organic matter and ammoniacal nitrogen of these modified carriers were 81 ± 1.9% and 86 ± 9.5%, respectively. The 4.5% increase in specific surface area contributed to slightly higher densities of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) on the modified carriers. The addition of modified plastic caps as carriers in both anoxic and oxic tanks helped improved the total nitrogen reduction efficiency from 77.9 ± 4.0% to 82.3 ± 7.3%. Full-length 16 S rRNA metagenomic barcoding analysis revealed that the microbial communities within the modified carriers from oxic tank were highly diverse, abundant, and evenly distributed with 399 bacterial genera, 626 bacterial species detected. In addition to <i>Caldilinea</i> and <i>Nitrospira</i>, other prevalent species included <i>Romboutsia</i> (3.02%) and <i>Thermomonas</i> (2.96%), the latter known for its nitrate-reducing capabilities. The metagenomic analysis and treatment efficiency confirmed the possibility of using this kind of carriers for nitrogen removal from nutrient-rich wastewater.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 10","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02929-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933321","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":"Attention-based self-learning graph neural networks for robust multi-node water quality prediction","authors":"Bo Zhu,&nbsp;Chen Wu,&nbsp;Jiyun Li,&nbsp;Yezheng Liu,&nbsp;Cuiqin Yin,&nbsp;Wei Wang,&nbsp;Xiao Zhou","doi":"10.1007/s13201-026-02962-3","DOIUrl":"10.1007/s13201-026-02962-3","url":null,"abstract":"<div><p>Water quality prediction plays a critical role in water supply systems (WSSs) management such as optimizing chlorination, identifying anomalies, and tracing pollutants. In contrast to traditional single-objective prediction, multi-node prediction holds increasing importance given the rising monitoring capabilities today. Despite several existing studies on multi-node water quality prediction, how to leverage the spatiotemporal relationships among monitoring point data to enhance prediction accuracy and stability, and how to ensure model robustness under abnormal conditions such as missing data are still problems that have not yet been fully addressed. This study proposes an Attention-based Multivariate Time Series Graph Neural Network (AMTGNN) for multi-node water quality prediction, which dynamically learns the correlations between different locations to jointly model temporal and spatial dependencies for improved prediction accuracy and robustness. The model is tested on a real-world urban-rural WSS. A comparative analysis with other state-of-the-art models is conducted, and the impact of different inputs and the robustness under missing data scenarios are also investigated. Experimental results show that AMTGNN achieves an <span>(:{R}^{2})</span> of 0.92 when predicting residual chlorine variation 3 h ahead, and maintains a Mean Absolute Percentage Error (MAPE) below 5.5% with an <span>(:{R}^{2})</span> above 0.85 even under 90% missing data, demonstrating robustness under realistic and imperfect WSS conditions.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 10","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02962-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148926854","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":"Assessment of groundwater quality for drinking purposes using hybrid approaches standard weights, trapezoidal fuzzy number-AHP, water quality index and GIS: a case study of Chennai district, Tamil Nadu, India","authors":"Prabu Babu,&nbsp;Saurabh Chandra Maury","doi":"10.1007/s13201-026-02959-y","DOIUrl":"10.1007/s13201-026-02959-y","url":null,"abstract":"<div><p>Groundwater is a vital resource for human consumption and industrial activities. However, anthropogenic activities have become a major source of groundwater contamination, posing a significant environmental concern due to increasing pollution levels. Approximately 90% of the population in the study area depends on groundwater for domestic and other purposes. Therefore, maintaining groundwater quality is essential for ensuring safe drinking water and protecting public health. Accordingly, the present study focuses on Chennai District, Tamil Nadu, India. The analyzed groundwater quality parameters include total dissolved solids, nitrate, calcium, magnesium, sodium, potassium, chloride, sulfate, bicarbonate, fluoride, pH, and electrical conductivity. Parameter weights were determined using the Trapezoidal Fuzzy Number Analytic Hierarchy Process (TRFN-AHP) and standard weighting methods. The main objective of this study is to evaluate groundwater quality for drinking purposes using a hybrid approach standard weights, TRFN-AHP, Water Quality Index (WQI), and Geographic Information System (GIS). Spatial distribution maps of groundwater quality parameters and WQI were generated using the Inverse Distance Weighting (IDW) interpolation method in ArcGIS. The WQI values, ranging from 50 to 300, were classified into five categories: excellent, good, poor, very poor, and unsuitable for drinking purposes. The results based on standard weights revealed that 7.90% of the samples were classified as excellent groundwater quality, 65.82% as good groundwater quality, 21.73% as poor groundwater quality, 1.95% as very poor groundwater quality, and 2.60% as unsuitable for drinking purposes. Similarly, the TRFN-AHP method revealed that 7.10% of the samples were classified as excellent groundwater quality, 62.82% as good groundwater quality, 25.93% as poor groundwater quality, 0.70% as very poor groundwater quality, and 3.45% as unsuitable for drinking purposes. To evaluate predictive performance, Area Under the Curve (AUC) was used. The obtained AUC value was 0.76 for the WQI based on standard weights, whereas the AUC value for the TRFN-AHP-based WQI was 0.87, indicating high predictive accuracy. The results indicate that the TRFN-AHP approach demonstrated improved performance for groundwater quality assessment. Furthermore, the hybrid TRFN-AHP approach showed effectiveness in evaluating groundwater quality through the WQI framework. By incorporating uncertainty and expert judgment into the weighting process, the method provided a more comprehensive assessment of groundwater quality conditions. The predictive performance of the TRFN-AHP-based WQI suggests its potential to identify spatial variations in water quality under the investigated conditions. Therefore, the proposed method may serve as a useful decision-support tool for groundwater quality assessment, monitoring, and sustainable water resource management.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 10","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02959-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895154","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":"Solar activation of periodate for the effective degradation of reactive blue 222 dye: optimization, generated by-products, and real textile wastewater","authors":"Salma Tarek,&nbsp;Moharram Fouad,&nbsp;Hani Mahanna,&nbsp;Mahmoud Samy","doi":"10.1007/s13201-026-02956-1","DOIUrl":"10.1007/s13201-026-02956-1","url":null,"abstract":"<div><p>This article presents the activation of periodate (PI) under simulated sunlight (SSL) for the removal of reactive blue 222 dye (RB-222). Further, the degradation system was evaluated using real industrial effluents, and the utilization of natural sunlight as a light source for the degradation of RB-222 dye was assessed, both of which have been poorly explored in the literature. The optimization study revealed that a removal efficiency of 99.9% could be accomplished at an initial dye concentration of 5 mg/L, PI concentration of 4.7 mM, temperature of 70 °C, light intensity of 0.013 W/cm², pH 7, and reaction time of 90 min. The replacement of SSL with natural sunlight under the optimal conditions could achieve a removal percentage of 98%, affirming the applicability of the remediation system under sunlight. Quenching tests exhibited the main participation of iodate radicals and singlet oxygen in the degradation system over other radicals. The degradation system could remove 69.4% of the total organic carbon in real effluents compiled from a textile factory. The generated by-products were determined, and the toxicity assessment by Ecological Structure Activity Relationships affirmed the toxicity reduction of the generated intermediates. The proposed remediation system is promising for the practical application due to its high performance under sunlight and cost-effectiveness.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 10","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02956-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895157","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":"Innovative method to estimate groundwater levels using remote sensing data","authors":"Ahmed S. Nour-Eldeen,&nbsp;Marwa Khairy,&nbsp;Ismail Abd-Elaty,&nbsp;Abdelazim Negm,&nbsp;Hickmat Hossen","doi":"10.1007/s13201-026-02951-6","DOIUrl":"10.1007/s13201-026-02951-6","url":null,"abstract":"<div><p>Effective monitoring of surface and groundwater resources is essential for sustainable water management, especially in arid and semi-arid regions where water scarcity poses a critical challenge. Remote sensing technologies have emerged as powerful tools for assessing hydrological conditions with reduced cost and improved spatial and temporal coverage. This study provides important insights into an innovative method for estimating water levels and depths in wells using feeding lakes and remote sensing data, with a focus on the effectiveness of spectral bands and model performance. Unlike previous studies that focused mainly on bathymetric estimation, the proposed approach integrates surface water depth estimation with groundwater fluctuation analysis in nearby wells using a simplified regression-based framework. The wells, from which water depths were estimated, are located in the Kima area of Aswan, Egypt. A linear regression model was successfully developed to estimate pond water depths over different time periods using Sentinel-2 Level 1 C reflectance data and in-situ depth measurements as reference, highlighting the vital role of field data in model calibration and validation. The analysis revealed that bands B2 and B3 showed the strongest correlation with measured depths, with R² values exceeding 0.88, indicating their suitability for bathymetric applications. Model validation showed slightly higher RMSE values in the validation phase (&gt; 0.35 m) compared to calibration, with some estimates exceeding 1–2 m, confirming the model’s reliability. A strong correlation (average cross-correlation coefficient of 0.8) was also observed between pond surface water levels and groundwater levels in nearby wells, reflecting the dynamic interaction between surface and groundwater systems. Applying the methodology to historical data (2015–2023) yielded promising results, demonstrating its potential for long-term monitoring and environmental assessment. These findings highlight the value of remote sensing in hydrological studies, offering a cost-effective, scalable solution for groundwater management and sustainable water resource planning.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 10","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02951-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884033","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":"Silver-Molybdate@KaoliniteNanocomposites for efficient and reusable inorganic cation decontamination from wastewater: a cost-effective sustainable approach for Cr(VI) and Cu(II) elimination","authors":"Aya M. Matloob,&nbsp;Tahany Mahmoud,&nbsp;Mahmoud F. Mubarak,&nbsp;Asmaa S. Morshedy","doi":"10.1007/s13201-026-02961-4","DOIUrl":"10.1007/s13201-026-02961-4","url":null,"abstract":"<div><p>The persistent threat of toxic heavy metals like Cr(VI) and Cu(II) in water systems demands advanced, cost-effective solutions beyond conventional adsorbents such as activated carbon, clay minerals, and ion-exchange resins, which often suffer from limited adsorption capacity, slow kinetics, poor stability under acidic or basic conditions, and high regeneration costs. Here, we engineer a robust Ag₂MoO₄@Kaolinite nanocomposite to overcome these limitations, delivering exceptional adsorption performance and reusability. Advanced characterizations (XRD, FT-IR, SEM, and TEM) confirm the nanocomposite’s optimized structure and active sites. In batch tests, the material achieves &gt; 90% removal of Cr(VI) (pH 2) and Cu(II) (pH 6), reducing Cr(VI) concentrations from 50 mg/L to below 0.5 mg/L and Cu(II) from 50 mg/L to below 2.5 mg/L at optimal adsorbent doses, outperforming traditional methods. Comparative plots of absolute residual concentrations as a function of adsorbent dose are provided to clearly illustrate the treatment efficacy and compliance with water quality standards. Adsorption follows pseudo-second-order kinetics and the Langmuir isotherm, indicating chemisorption-driven monolayer uptake. Remarkably, the nanocomposite retains 82–88% efficiency after five regeneration cycles, demonstrating unmatched durability compared to conventional adsorbents that typically lose significant capacity within fewer cycles. Its stability across diverse conditions (variable doses, concentrations, and competing ions) ensures real-world applicability. A breakthrough in cost-efficiency synthesis at just $64.50–89.00 per 100 g and 91–98% operational savings over conventional systems positions this innovation as a scalable, economically viable solution. By merging high performance with sustainability, Ag₂MoO₄@Kaolinite bridges the gap from lab-scale promise to industrial adoption, offering a transformative tool for combating heavy metal pollution.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 10","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02961-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884034","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":"A novel hybrid remote sensing–machine learning framework for multiday soil moisture forecasting","authors":"Saeed Samadianfard,&nbsp;Elnaz Bayat Khajeh,&nbsp;Neda Beirami,&nbsp;Behnam Khorrami,&nbsp;Orhan Gündüz","doi":"10.1007/s13201-026-02952-5","DOIUrl":"10.1007/s13201-026-02952-5","url":null,"abstract":"<div><p>The accurate forecasting of surface soil moisture (SSM) for multiple time steps is vital in the early warning systems of drying and flooding, irrigation scheduling, and hydrological modelling. The current study presents an innovative hybrid structure called ASHA–LSTM–XGB that provides 1, 3, 5, and 7-day forecasting for SSM by combining Long Short-Term Memory networks (LSTM), eXtreme Gradient Boosting (XGBoost), and the Asynchronous Successive Halving Algorithm (ASHA). The proposed model was tested and validated over Izmir Province in Western Anatolia, Türkiye, using satellite mapping of the SMAP Level 4 SSM dataset and 11 ERA5-Land environmental predictors from April 2015 to October 2025. The obtained results clearly indicate that all proposed hybrid approaches showed better performance than the traditional LSTM model for any horizon of the prediction. For the shortest term, BO-LSTM-XGB was found to be the most accurate method (NRMSE = 0.2151, R² = 0.8720). Meanwhile, the best combination of high prediction accuracy and stability was achieved by ASHA-LSTM-XGB, which demonstrated the best performance in the medium-term period and beyond (NRMSE = 0.2581, R² = 0.8153; NRMSE = 0.2666, R² = 0.8027 at 5- and 7-day horizons, respectively). Thus, these results demonstrate the efficiency of integrating sequential learning, non-linear ensembling, and optimal hyperparameter tuning for forecasting problems. Moreover, the results of the ablation study and analysis using SHAP values support the finding that the two most important predictor variables for SSM were the surface temperature and surface radiation variables. The integration of ASHA and LSTM-XGB has resulted in greater stability and generalization of the models. This study is considered the first application of the combined optimization-deep learning-ensemble architecture to forecast multi-step SSM. The proposed method performed better than the conventional LSTM algorithm in all forecasting scenarios and showed robust performance even at a 7-day forecasting lead time, showing promise for applications in short-range soil moisture prediction and environmental monitoring studies.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 10","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02952-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148883874","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":"Integrated morphometric and machine learning approach for prioritization of erosion-prone sub-watersheds","authors":"Padala Raja Shekar,&nbsp;M. Surya Prasanth,&nbsp;Arun P. S,&nbsp;Aneesh Mathew,&nbsp;Malay Pramanik,&nbsp;Fahdah Falah Ben Hasher,&nbsp;Mohamed Zhran","doi":"10.1007/s13201-026-02941-8","DOIUrl":"10.1007/s13201-026-02941-8","url":null,"abstract":"<div><p>Soil erosion is a critical global environmental challenge that threatens food security and undermines sustainability efforts worldwide. The accelerating rate of erosion leads to severe land degradation, making it essential to adopt strategic and localized resource management approaches. Prioritizing sub-watersheds allows for more effective conservation planning, as each watershed exhibits unique hydrological and geomorphological characteristics. This study introduces a comprehensive framework for prioritizing sub-watersheds (SWs) in the Aran Basin using an integrated approach. It combines morphometric analysis with multiple criteria decision-making (MCDM) methods—namely VIKOR (visekriterijumsko kompromisno rangiranje), TOPSIS (technique for order preference by similarity to ideal solution), and ARAS (additive ratio assessment)—alongside a machine learning model, support vector machine (SVM), to ensure a more robust and consensus-driven prioritization. The novelty of this research lies in the innovative integration of machine learning with morphometric and MCDM techniques, resulting in a scientifically rigorous and unified ranking of sub-watersheds. Based on the integrated analysis, SW1, SW8, SW9, and SW12 are identified as high-priority sub-watersheds for soil erosion control, collectively covering approximately 30% of the total watershed area, indicating substantial spatial susceptibility. SW2, SW4, SW10, and SW11 fall into the medium-priority category, while SW3, SW5, SW6, and SW7 are classified as low-priority zones. Aligned with sustainable development goal 15 (life on land), this methodology supports informed decision-making for targeted soil conservation and sustainable land management, ultimately contributing to the long-term resilience of terrestrial ecosystems.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"16 10","pages":""},"PeriodicalIF":5.7,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-026-02941-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884035","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}