Water demand forecasting based on multi-rainfall gauging stations using stand-alone soft computing techniques with improved novel hybrid paradigms

Water demand forecasting based on multi-rainfall gauging stations using stand-alone soft computing techniques with improved novel hybrid paradigms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Water demand forecasting based on multi-rainfall gauging stations using stand-alone soft computing techniques with improved novel hybrid paradigms Tarek Merabtene, A. G. Usman, Berna Uzun, Dilber Uzun Ozsahin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6641795/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Water Resources Management → Version 1 posted 5 You are reading this latest preprint version Abstract Water demand forecasting is crucial for effective water resource planning and management. This study presents a reliable and cost-effective approach to model and predict water demand using rainfall measurements from various stations. Four stand-alone methods were employed: Support Vector Regression (SVR), Gaussian Process Regression (GPR), Least Square Boost (LSBOOST), and Stepwise Linear Regression (SWLR). To enhance prediction performance, novel hybrid models were developed, combining SWLR with SVR, GPR, and LSBOOST. The effectiveness of both the stand-alone and hybrid methods was assessed using four statistical metrics: Nash-Sutcliffe efficiency (NSE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (PCC), and Mean Absolute Percentage Error (MAPE). Results showed that none of the stand-alone techniques could effectively predict the water demand due to its complex nature. In contrast, the hybrid techniques, particularly SWLR-GPR and SWLRLSBOOST, demonstrated robust performance, achieving a minimum NSE of 0.95 during both calibration and validation. Graphical analyses, including time series plots and a 2-dimensional Taylor diagram (2D-TD), illustrated these models' ability to capture daily demand fluctuations. The hybrid models significantly outperformed the stand-alone techniques, improving prediction accuracy by over 81% to 88% in calibration and validation phases, respectively, despite relying solely on rainfall as input. This study underscores the potential of hybrid modeling approaches in enhancing water demand forecasting accuracy. Water demand rainfall hybrid paradigms soft computing time series Full Text Supplementary Files Summary1.pdf Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Water Resources Management → Version 1 posted Editorial decision: Major revisions 19 Aug, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers invited by journal 10 Jul, 2025 Editor assigned by journal 16 May, 2025 First submitted to journal 15 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6641795","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483526361,"identity":"285e394d-6398-4aea-84e8-2507a3796793","order_by":0,"name":"Tarek Merabtene","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYNACNgYZBmYG9h8fQGx2IrXwALUwSM4AsZmJ1gIE0mCSkBb+aacTPxeU2fHws7M/MLb5tU2ej5mB8cPHHNxaJG7nbpaecS6ZR7KZxyA5t++2YRszA7PkzG14rLmdu0Gat42Zx+AwD8Ph3J7bjEAtbMy8eLTIA235zdtWD9TC/rDZsue2PUEtBrdztwFtOQzUwmDMzPDjdiJBLYZALdY8546D/GLG2NtwO7mNmbEZr1/kgA67zVNWLcfPf/wZw48/t23ntzcf/PARn/dRAGMbmGwgVj0I/CFF8SgYBaNgFIwUAADqXklYkPaNpgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Sharjah","correspondingAuthor":true,"prefix":"","firstName":"Tarek","middleName":"","lastName":"Merabtene","suffix":""},{"id":483526362,"identity":"2a4100fe-3113-4b2c-90a0-9cb3544394fa","order_by":1,"name":"A. 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This study presents a reliable and cost-effective approach to model and predict water demand using rainfall measurements from various stations. Four stand-alone methods were employed:\nSupport Vector Regression (SVR), Gaussian Process Regression (GPR), Least Square Boost (LSBOOST), and Stepwise Linear Regression (SWLR). To enhance prediction performance, novel hybrid models were developed, combining SWLR with SVR, GPR, and LSBOOST. The effectiveness of both the stand-alone and hybrid methods was assessed using four statistical metrics: Nash-Sutcliffe efficiency (NSE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (PCC), and Mean Absolute Percentage Error (MAPE). Results showed that none of the stand-alone techniques could effectively predict the water demand due to its complex nature. In contrast, the hybrid techniques, particularly SWLR-GPR and SWLRLSBOOST, demonstrated robust performance, achieving a minimum NSE of 0.95 during both calibration and validation. 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