Analysis on temporal variability of monsoon rainfall using ARIMA Model and ANN-LSTM

Analysis on temporal variability of monsoon rainfall using ARIMA Model and ANN-LSTM | 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 Analysis on temporal variability of monsoon rainfall using ARIMA Model and ANN-LSTM Rahul Grover, Siddhartha Sharma, Pankaj Bajaj This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3975791/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 In this study, machine learning algorithms employed for predicting the weather conditions of the upcoming day with a particular focus on forecasting rain, which serves as a crucial indicator. To identify the most reliable rain-related attributes, most of the researchers utilize line charts, matrix graphs, and scatterplot graphs for visualization and analysis. The investigation reveals several attribute pairs demonstrating a significant degree of similarity and correlation. In the modeling phase, researchers employ elementary models such as decision trees, and regression analysis to assess their fundamental prediction capabilities. The resulting accuracy rate found favourable sometimes on statistics platform. Notably, during the visualization stage, a subtle pattern emerges: samples experiencing rainfall today exhibit a slightly elevated probability of rain the following day. Temporal variability in rainfall refers to the fluctuations and changes in precipitation patterns over time. It encompasses the temporal distribution, intensity, and duration of rainfall events, reflecting the dynamic nature of weather systems. Understanding temporal variability is crucial for effective water resource management, agriculture, and climate impact assessment. Consequently, our attempt to analyze the influence of historical weather on forecasting using ARIMA and ANN-LSTM. The rainfall predictions generated by these models are deemed accurate based on statistical analysis and algorithm simulation. Monsoon rainfall Temporal Variability LSTM Deep Learning ARIMA ACF PACF 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. 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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-3975791","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274519320,"identity":"a7a94d76-4830-4782-8830-7b8caef175fe","order_by":0,"name":"Rahul Grover","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYFACHgaGBxAWI4RmZ24grCUBwmI2gFCMxGthkyBKi3n72WMSCb/uyRkc731WdTPncLTBYcbGzwU1DHLm/QuwapE5k5cmkdhXbGxw5rjZ7dxth3M3HGZslp5xjMFY5sYDrFokGHLMJBJ7EhJnzkhjg2lpkOZhY0icIXEAuxb+N1At85+xFcNs+c3zD48WCaAtCT8SEvsl2NiYoVrapHnbgFr4sQeDhMQbY4vEhgRjfp40Zuncbem5M4FarHn7JIwlJLCHmAR/juGND38S5NjYjzF+zt1mndt3vPnwbZ5vNnIS/NgdBgaMbdjChUEiAbcWhj9YRfHZMgpGwSgYBSMJAAC+rF2leLYpnAAAAABJRU5ErkJggg==","orcid":"","institution":"Maharishi Markandeshwar University, Mullana","correspondingAuthor":true,"prefix":"","firstName":"Rahul","middleName":"","lastName":"Grover","suffix":""},{"id":274519321,"identity":"fe667cff-b324-4eb5-8faf-6fa9d131eb86","order_by":1,"name":"Siddhartha Sharma","email":"","orcid":"","institution":"Maharishi Markandeshwar University, Mullana","correspondingAuthor":false,"prefix":"","firstName":"Siddhartha","middleName":"","lastName":"Sharma","suffix":""},{"id":274519322,"identity":"9cf0aa36-3604-48fc-aba2-d0fe0b49b638","order_by":2,"name":"Pankaj Bajaj","email":"","orcid":"","institution":"Maharishi Markandeshwar University, Mullana","correspondingAuthor":false,"prefix":"","firstName":"Pankaj","middleName":"","lastName":"Bajaj","suffix":""}],"badges":[],"createdAt":"2024-02-21 14:00:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3975791/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3975791/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52403106,"identity":"4e7fbb2a-ae57-432d-b44c-ac22e9da7567","added_by":"auto","created_at":"2024-03-11 07:54:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":926846,"visible":true,"origin":"","legend":"","description":"","filename":"researchpaperARIMAandLSTM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3975791/v1_covered_96d52eba-00f6-42b7-b9f6-d1c20d36deea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis on temporal variability of monsoon rainfall using ARIMA Model and ANN-LSTM","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":"Monsoon rainfall, Temporal Variability, LSTM, Deep Learning, ARIMA, ACF, PACF","lastPublishedDoi":"10.21203/rs.3.rs-3975791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3975791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In this study, machine learning algorithms employed for predicting the weather conditions of the upcoming day with a particular focus on forecasting rain, which serves as a crucial indicator. 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