Rainfall Projections for the Brazilian Legal Amazon using Recurrent Neural Networks

Rainfall Projections for the Brazilian Legal Amazon using Recurrent Neural Networks | 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 Rainfall Projections for the Brazilian Legal Amazon using Recurrent Neural Networks Luiz Augusto Ferreira Monteiro, José Francisco Oliveira-Júnior, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4601946/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 Rainfall in the Brazilian Legal Amazon (BLA) is vital for climate and water resource management. This research uses spatial downscaling and validated rainfall data from the National Water and Sanitation Agency (ANA) to ensure accurate rain projections with artificial intelligence. Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) were employed to forecast rainfall from 2012 to 2020. The RNN model showed strong alignment with observed patterns, accurately predicting rainfall seasonality. However, median comparisons revealed fair approximations with discrepancies. The Root Mean Square Error (RMSE) ranged from 6.7mm to 11.2mm, and the coefficient of determination (R²) was low in some series. Extensive analyses showed low Wilmott agreement and high Mean Absolute Percentage Error (MAPE), highlighting limitations in projecting anomalies and days without rain. Despite challenges, the study lays a foundation for future advancements in climate modeling and water resource management in the BLA. Artificial Intelligence Spatial Downscaling Rain Projection Data Quality Clustering Brazilian Legal Amazon Full Text Additional Declarations No competing interests reported. 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-4601946","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322309628,"identity":"d415e724-73cc-4d7b-8f5e-df539e82b59f","order_by":0,"name":"Luiz Augusto Ferreira 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This research uses spatial downscaling and validated rainfall data from the National Water and Sanitation Agency (ANA) to ensure accurate rain projections with artificial intelligence. Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) were employed to forecast rainfall from 2012 to 2020. The RNN model showed strong alignment with observed patterns, accurately predicting rainfall seasonality. However, median comparisons revealed fair approximations with discrepancies. The Root Mean Square Error (RMSE) ranged from 6.7mm to 11.2mm, and the coefficient of determination (R\u0026sup2;) was low in some series. Extensive analyses showed low Wilmott agreement and high Mean Absolute Percentage Error (MAPE), highlighting limitations in projecting anomalies and days without rain. 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