Comparative Evaluation of Statistical and Machine Learning Models for Spatiotemporal Groundwater Level Forecasting in a Climate-Sensitive Coastal Aquifer

Comparative Evaluation of Statistical and Machine Learning Models for Spatiotemporal Groundwater Level Forecasting in a Climate-Sensitive Coastal Aquifer | 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 Comparative Evaluation of Statistical and Machine Learning Models for Spatiotemporal Groundwater Level Forecasting in a Climate-Sensitive Coastal Aquifer Tajwar Taskin, Sajia Afrin Munia, H. M. Rasel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9336246/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Coastal groundwater systems and deltaic systems portray a high level of spatiotemporal variability due to the seasonality in climate and human-induced stressors, and the assessment of the groundwater level (GWL) remains a consistent challenge. This paper gives a comparative data-driven modeling model to predict the level of groundwater variations with long-term records of the monitoring wells of Barishal, Bangladesh. The framework combines both a classical seasonal time-series model (SARIMA), deep learning (Long Short-Term Memory; LSTM) and ensemble machine learning (XGBoost and Random Forest) in order to model both linear and nonlinear groundwater dynamics. These methods were used in forecasting the changes in groundwater level (GWL) in susceptible wells in Barishal. RMSE, MAE, R 2, and Nash Sutcliffe Efficiency (NSE) were used as a measure of model performance. Findings show that LSTM and Random Forests models are better than traditional statistical models at modeling complex groundwater swings, especially in longer predictions. GIS-based spatial analysis also indicates how significant are regional heterogeneity in groundwater responses and the impact of climatic drivers in local groundwater variation. The results indicate that multi-model data-driven models can be useful in the evaluation of groundwater in hydro-climatically complex and data-constrained coastal areas that can be valuable information in the area of groundwater monitoring and management. Groundwater fluctuation Barishal Climate change SARIMA LSTM XGBoost Random Forest Groundwater level GIS mapping Climatic fact Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviews received at journal 06 May, 2026 Reviews received at journal 03 May, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers invited by journal 16 Apr, 2026 Editor assigned by journal 08 Apr, 2026 Submission checks completed at journal 08 Apr, 2026 First submitted to journal 06 Apr, 2026 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. 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