Spatiotemporal Neural Networks for Forecasting Climate-Related Disasters in Sub-Saharan Africa

Spatiotemporal Neural Networks for Forecasting Climate-Related Disasters in Sub-Saharan Africa | 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 Spatiotemporal Neural Networks for Forecasting Climate-Related Disasters in Sub-Saharan Africa Tayo P. Ogundunmade, Christopher G. Udomboso, Aderonke Ajefolakemi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9234437/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 Climate-related disasters such as floods, droughts, and heatwaves are significant threats to livelihoods and development in Sub-Saharan Africa. Accurate prediction of these hazards is difficult due to inadequate meteorological data and spatial inconsistencies, as well as the intricate interaction of various climatic parameters. In this paper, spatiotemporal neural networks (STNNs) are explored as a tool in disaster prediction, and their various architectures, namely Convolutional LSTMs (ConvLSTMs), Graph Neural Networks (GNNs), and Transformers, are compared, and a novel hybrid fusion framework is proposed. Datasets from satellite imagery (MODIS and Sentinel), reanalysis (ERA5 and CHIRPS), and ground-based weather stations are preprocessed to create multivariate input datasets consisting of rainfall, temperature, soil moisture, and vegetation indices. Composite loss functions are utilized in training all models, and a series of metrics are utilized to evaluate performance, namely RMSE, precision, recall, F1-score, and lead-time accuracy. k-fold cross-validation is utilized in validating results in regional case studies in Nigeria, Ethiopia, and South Africa. It is established that ConvLSTMs are optimal in flood prediction, GNNs in drought monitoring, and Transformers in heatwave prediction. Nevertheless, it is also established that a hybrid fusion model is superior to all individual models in all metrics, achieving a minimum RMSE of 0.31, a maximum F1-score of 0.89, and a maximum lead-time accuracy of 8.1 days. Regional evaluations also demonstrate that hybrid STNNs are adaptable to various regional hazards, and variable-based evaluations indicate that rainfall and soil moisture are key predictors of disaster occurrences. Climate Analysis and Modeling Spatiotemporal Neural Networks Extreme Weather Forecasting Sub-Saharan Africa Floods Droughts Climate Resilience Full Text Additional Declarations The authors declare no competing interests. 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-9234437","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612683923,"identity":"2f761e99-eb65-4bb3-ac26-67557cbb5ad1","order_by":0,"name":"Tayo P. 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