{"paper_id":"0db155ac-2adf-40fa-b2b2-6886cd90b079","body_text":"Extreme rainfall forecasting using a hybrid model approach - A case study of the Ajay River basin | 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 Extreme rainfall forecasting using a hybrid model approach - A case study of the Ajay River basin Shivanand Mandraha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4757305/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 Extreme rainfall event forecasting is important as these events are responsible for causing floods, landslides, and other hazards with substantial socio-economic consequences. The intricate nature of rainfall occurrences makes it more difficult to forecast accurately, especially when it comes to extreme rainfall. This study investigates the application of the Quantile Perturbation Method (QPM) along with the Long Short-Term Memory (LSTM) networks to forecast extreme rainfall anomalies. This methodology utilizes the strength of QPM to decipher oscillations in time series of extreme rainfall to identify anomalies, which are then forecasted using LSTM. The model was developed for the Ajay River basin as a case study based on historical rainfall data from 1901–2022. To determine the best model, several experiments with various configurations were conducted. Performance metrics such as Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and root mean square error (RMSE) were utilized for model evaluations. The QPM-LSTM model was compared against other combined machine learning models, including Artificial Neural Networks (ANN) and Support Vector Regression (SVR). The investigation demonstrated satisfactory predictive performance by the QPM-LSTM model, achieving NSE, R, and RMSE values of 0.87, 0.93, and 7.26, respectively. Compared to the other evaluated models, these results highlighted the potential of the QPM-LSTM model as a valuable tool for forecasting extreme rainfall anomalies, offering significant benefits for water resource management and other sectors vulnerable to extreme rainfall events. Machine learning Extreme rainfall forecasting LSTM Quantile Perturbation Method 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-4757305\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":333900447,\"identity\":\"e62caef2-3003-4e75-867c-918efc53b344\",\"order_by\":0,\"name\":\"Shivanand Mandraha\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIie2RsWrDMBCGTwisxSWrRIa+gkwgtOCHkZd065LFQ2nPGJQlkK0k5GUiBO2S7A2BQvALpJtLTals0q1yhy4d9A1Cd+jj/kMAgcC/hMxOAPxBswI3rpTAujbvUah2R0oWsTkr9FeFtcqErJaqK78VP5ePRld5Y6ncV4Wp89fRgAI51XB161PkIZslO20jd0Ebb6djUQIVc+BTrzLMUBTucacQrVJpAYZulwx9wdZOwcZyuTdo3j87hX70KXDItMBoIsWS4OYC1dgpUe8Ul6dMCp2qQdzu8qRGoiT6ei77gt0cj9hwFbHn6q2+U8mClfalzu/9wX6AYPs9gUAgEPgDX081WgSVlWn4AAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Indian Institute of Science Education and Research Kolkata\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Shivanand\",\"middleName\":\"\",\"lastName\":\"Mandraha\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-07-17 15:32:17\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4757305/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4757305/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":73576096,\"identity\":\"be5d291f-cb75-4a79-a5f7-0c1e5a38c9d5\",\"added_by\":\"auto\",\"created_at\":\"2025-01-11 21:01:19\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":662164,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ManuscriptSERRA.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4757305/v1_covered_02ebde22-c161-47c3-a140-25064b36de28.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Extreme rainfall forecasting using a hybrid model approach - A case study of the Ajay River basin\",\"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\":\"info@researchsquare.com\",\"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\":\"Machine learning , Extreme rainfall forecasting, LSTM , Quantile Perturbation Method\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4757305/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4757305/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eExtreme rainfall event forecasting is important as these events are responsible for causing floods, landslides, and other hazards with substantial socio-economic consequences. The intricate nature of rainfall occurrences makes it more difficult to forecast accurately, especially when it comes to extreme rainfall. This study investigates the application of the Quantile Perturbation Method (QPM) along with the Long Short-Term Memory (LSTM) networks to forecast extreme rainfall anomalies. This methodology utilizes the strength of QPM to decipher oscillations in time series of extreme rainfall to identify anomalies, which are then forecasted using LSTM. The model was developed for the Ajay River basin as a case study based on historical rainfall data from 1901\\u0026ndash;2022. To determine the best model, several experiments with various configurations were conducted. Performance metrics such as Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and root mean square error (RMSE) were utilized for model evaluations. The QPM-LSTM model was compared against other combined machine learning models, including Artificial Neural Networks (ANN) and Support Vector Regression (SVR). The investigation demonstrated satisfactory predictive performance by the QPM-LSTM model, achieving NSE, R, and RMSE values of 0.87, 0.93, and 7.26, respectively. Compared to the other evaluated models, these results highlighted the potential of the QPM-LSTM model as a valuable tool for forecasting extreme rainfall anomalies, offering significant benefits for water resource management and other sectors vulnerable to extreme rainfall events.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Extreme rainfall forecasting using a hybrid model approach - A case study of the Ajay River basin\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-08-16 03:04:21\",\"doi\":\"10.21203/rs.3.rs-4757305/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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}}],\"origin\":\"\",\"ownerIdentity\":\"ba767e56-5057-4d5a-a25f-75d06cfd2a8f\",\"owner\":[],\"postedDate\":\"August 16th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-01-11T20:53:12+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-08-16 03:04:21\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4757305\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4757305\",\"identity\":\"rs-4757305\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}