Enhancing Drought Forecasting Using GNN-LSTM with Attention Mechanism: A study of Jaisalmer district, Rajasthan

Enhancing Drought Forecasting Using GNN-LSTM with Attention Mechanism: A study of Jaisalmer district, Rajasthan | 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 Enhancing Drought Forecasting Using GNN-LSTM with Attention Mechanism: A study of Jaisalmer district, Rajasthan Ritu Khandelwal, Hemlata Goyal, Rajveer Singh shekhawat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5704094/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 Droughts severely effect food production, water supply, and environment, demanding accurate prediction for mitigation. This study addresses the critical challenge of drought prediction by leveraging advanced deep learning methods to improve forecast accuracy. The research focuses on developing a Graph Neural Network-Long Short-Term Memory (GNN-LSTM) model integrated with an attention mechanism to analyze the relationship between precipitation data and drought indicators of VCI, TCI, VHI, and SPI in Jaisalmer, Rajasthan, India, from 1991 to 2023. The study acknowledges the best mapping of precipitation to compute SPI, and investigates the spatial correlation with VCI, TCI, VHI, and SPI drought indices. The proposed model GNN-LSTM with attention, scored spatial dependency in dynamic way using graph and improves long range temporal learning rate with LSTM architecture via attention to lead better accuracy and robustness in drought prediction in comparison to developed model architecture of Artificial Neural Network with Multi-Point Aggregation (ANN-MPA), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) etc. The GNN-LSTM model outperformed in distinguishing and predicting drought and non-drought regions with Mean Absolute Error (MAE) of ± 0.1906, Mean Squared Error (MSE) of ± 0.0982, Root Mean Squared Error (RMSE) of ± 0.03285, and an R-squared score of ± 0.8239. The main aim of this research is to contribute to decision making framework to mitigate the adverse effects of climate variability, enhanced agricultural planning and helps in early warning systems, allowing for proactive measures to manage and adapt to drought conditions, ultimately contributing to a sustainable environment. drought prediction sustainable environment SPI GNN-LSTM attention mechanism 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. 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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-5704094","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436112856,"identity":"bd8687f8-a058-47c7-8eaa-17c1afdde873","order_by":0,"name":"Ritu Khandelwal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACZhDBc4CBD0gd+PjPBkgxNh7Ar4UZooUNSB2cwZYG0tKAXwvEGogWZh62wwwQLh5g3s5/TIJB5o48G/vZgwdn8Jy3W9t+GGhLjU00Li0yh5nZJBh4nhm28eQlHPggcTt525lEoJZjabkNOLRIMIO1HGZsY8gxODjD4Hay2QGgFsaGwwS12LfxvzE4zJNwLtns/EPitCS2SeQAtRw4YGd2g7AtxhYJPIeT2yTeGByc2ZCcYHYDaEsCPr/wH3x442PPYdt+/hzjDx8b7OzNzqc/fPChxganFjBI7EFig1Um4FMOBj8QTHuCikfBKBgFo2DEAQCOs18kT1mEwwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9990-942X","institution":"Manipal University Jaipur","correspondingAuthor":true,"prefix":"","firstName":"Ritu","middleName":"","lastName":"Khandelwal","suffix":""},{"id":436112857,"identity":"2a339d59-f7e3-4187-8dff-6d61eb56be24","order_by":1,"name":"Hemlata Goyal","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Hemlata","middleName":"","lastName":"Goyal","suffix":""},{"id":436112858,"identity":"1c979569-9d11-4e07-b069-18383b229d2d","order_by":2,"name":"Rajveer Singh shekhawat","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Rajveer","middleName":"Singh","lastName":"shekhawat","suffix":""}],"badges":[],"createdAt":"2024-12-24 06:58:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5704094/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5704094/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81518836,"identity":"eaa2d269-23de-48d6-bac1-4f643866189a","added_by":"auto","created_at":"2025-04-28 07:44:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":875596,"visible":true,"origin":"","legend":"","description":"","filename":"Revisedmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5704094/v1_covered_16f0699f-2d71-4833-b554-0004ba2ff71a.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eEnhancing Drought Forecasting Using GNN-LSTM with Attention Mechanism: A study of Jaisalmer district, Rajasthan\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"drought prediction, sustainable environment, SPI, GNN-LSTM, attention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-5704094/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5704094/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDroughts severely effect food production, water supply, and environment, demanding accurate prediction for mitigation. This study addresses the critical challenge of drought prediction by leveraging advanced deep learning methods to improve forecast accuracy. The research focuses on developing a Graph Neural Network-Long Short-Term Memory (GNN-LSTM) model integrated with an attention mechanism to analyze the relationship between precipitation data and drought indicators of VCI, TCI, VHI, and SPI in Jaisalmer, Rajasthan, India, from 1991 to 2023. The study acknowledges the best mapping of precipitation to compute SPI, and investigates the spatial correlation with VCI, TCI, VHI, and SPI drought indices. The proposed model GNN-LSTM with attention, scored spatial dependency in dynamic way using graph and improves long range temporal learning rate with LSTM architecture via attention to lead better accuracy and robustness in drought prediction in comparison to developed model architecture of Artificial Neural Network with Multi-Point Aggregation (ANN-MPA), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) etc. The GNN-LSTM model outperformed in distinguishing and predicting drought and non-drought regions with Mean Absolute Error (MAE) of \u0026plusmn;\u0026thinsp;0.1906, Mean Squared Error (MSE) of \u0026plusmn;\u0026thinsp;0.0982, Root Mean Squared Error (RMSE) of \u0026plusmn;\u0026thinsp;0.03285, and an R-squared score of \u0026plusmn;\u0026thinsp;0.8239. 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