Advancing Daily Streamflow Forecasting in Snow-Fed Mountainous Region Using a Novel Hybrid SWAT-BiLSTM Approach

Advancing Daily Streamflow Forecasting in Snow-Fed Mountainous Region Using a Novel Hybrid SWAT-BiLSTM Approach | 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 Advancing Daily Streamflow Forecasting in Snow-Fed Mountainous Region Using a Novel Hybrid SWAT-BiLSTM Approach Khalil Ahmad, Mudassar Iqbal, Muhammad Atiq Ur Rehman Tariq, Muhammad Laiq Ur Rahman Shahid, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3918785/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Precise prediction of streamflow ensures reliable planning and management of water resources. Physical-based prediction models are prone to significant uncertainties due to the complexity of processes involved as well as due to the uncertainties in model parameters and parameterizations. This study evaluates the performance of daily streamflow prediction in Astore a snow-fed mountainous region, by coupling physical-based semi-distributed hydrological Soil and Water Assessment Tool (SWAT) with data-driven (DD) Bidirectional Long Short-Term Memory (BiLSTM) model. Firstly SWAT and BiLSTM models are calibrated individually then coupled in three modes; SWAT-D-BiLSTM: flows obtained from SWAT with default parameters values used as one of the input in BiLSTM, SWAT-T-BiLSTM: flows obtained from SWAT with three most sensitive parameters values used as one of the input in BiLSTM and SWAT-A-BiLSTM: flows obtained from SWAT with all sensitive parameters values used as one of the input in BiLSTM. Input selection for DD model was carried out by cross correlation analysis of temperature, precipitation, and total rainfall with streamflow. The calibration, validation, and prediction of coupled models are carried out for periods 2007–2011, 2012–2015 and 2017–2019, respectively. Prediction performance is evaluated based on Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R 2 ), and Percentage Bias (PBIAS). Temperature showed greater correlation of 0.7 at 1-day lag as compared to precipitation and total rainfall with streamflow at daily time scale. The results showed that integrated model SWAT-A-BiLSTM outperformed SWAT-T-BiLSTM followed by SWAT-D-BiLSTM, BiLSTM and SWAT respectively. This study recommends coupling of hydrological models facing uncertainties with DD models. BiLSTM Coupling Machine Learning Streamflow SWAT Full Text Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3918785","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270669381,"identity":"6add7c4b-6e5d-4bcb-9a0e-24b0a6cbca17","order_by":0,"name":"Khalil 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Physical-based prediction models are prone to significant uncertainties due to the complexity of processes involved as well as due to the uncertainties in model parameters and parameterizations. This study evaluates the performance of daily streamflow prediction in Astore a snow-fed mountainous region, by coupling physical-based semi-distributed hydrological Soil and Water Assessment Tool (SWAT) with data-driven (DD) Bidirectional Long Short-Term Memory (BiLSTM) model. Firstly SWAT and BiLSTM models are calibrated individually then coupled in three modes; SWAT-D-BiLSTM: flows obtained from SWAT with default parameters values used as one of the input in BiLSTM, SWAT-T-BiLSTM: flows obtained from SWAT with three most sensitive parameters values used as one of the input in BiLSTM and SWAT-A-BiLSTM: flows obtained from SWAT with all sensitive parameters values used as one of the input in BiLSTM. 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