Improvement of Forecasting Drought by Means of Various Machine Learning Algorithms and Wavelet Transformation

Improvement of Forecasting Drought by Means of Various Machine Learning Algorithms and Wavelet Transformation | 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 Improvement of Forecasting Drought by Means of Various Machine Learning Algorithms and Wavelet Transformation Türker Tuğrul, Mehmet Ali HINIS This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3855107/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jun, 2024 Read the published version in Acta Geophysica → Version 1 posted 5 You are reading this latest preprint version Abstract Defined as a decrease in average rainfall amounts, drought is one of the most insidious natural disasters. When it starts, people may not be aware of it, that's why droughts are difficult to monitor. Scientists have long been working to predict and monitor droughts. For this purpose, they have developed many methods such as drought indices one of which Standardized Precipitation Index (SPI) is. In this study, SPI to detect droughts and machine learning algorithms, support vector machines (SVM), artificial neural networks (ANN), random forest (RF), decision tree (DT), frequently used in the literature to predict droughts and 3 different statistical methods: correlation coefficient (r), Root Mean-square Error (RMSE), Nash-Sutcliffe Efficiency (NSE) Coefficient to investigate model performance values were used. Wavelet analysis was also applied to improve model performances. Konya closed basin located in the middle of Türkiye in terms of location and is among the leading regions of Turkey in terms of grain is one of the regions most affected by droughts in Türkiye. One of the most important water resources of the region is the Apa dam. It provides water to many fields which fertile land in its vicinity and is affected by droughts. Therefore, this region was determined as the study area. Meteorological data, total monthly precipitation, that could represent the region were obtained between 1955 and 2020 from general directorate of state water works and general directorate of meteorology. The results show that among the models analyzed with machine learning algorithms, the best results were obtained from M04 model whose input structure was created from SPI, different times steps, data delayed up to 5 months and total monthly precipitation data for time t-1. Among machine learning algorithms, SVM has achieved the most successful results in not only without wavelet transform (WT) but also with WT. Effective results were obtained from M04 in which SVM with WT was used (NSE = 0.9942, RMSE = 0.0764, R = 0.9971). drought drought modelling SPI wavelet transform dam management machine learning Full Text Cite Share Download PDF Status: Published Journal Publication published 30 Jun, 2024 Read the published version in Acta Geophysica → Version 1 posted Reviewers agreed at journal 23 Jan, 2024 Reviewers invited by journal 23 Jan, 2024 Editor invited by journal 21 Jan, 2024 Editor assigned by journal 17 Jan, 2024 First submitted to journal 10 Jan, 2024 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-3855107","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268919343,"identity":"b600fd89-bcde-458a-babd-b9de492b932f","order_by":0,"name":"Türker Tuğrul","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYJACxgYY60EFkGBmbsCpFAR42CBaJMC8hDMgLQgjiNCS2IZqK1ZgL99j9nFGxb06g/Nrn0kkzquN5m8HavlRsQ2PLTzGMzecKZYwuPHcTCJx2/HcGYcZGxh7ztzGq4XxYVsCUMsxNqCWY7kNQC3MjG2EtPyDaZlzLHc+UVo2NgC1nG8Dammoyd1AUMuxtGLGGccSJGfeYGO2SDh2IHcjUMtBfH5hbz68mbGnJoGf7/wxxhsfaupy550/fPDBjwrcWhBAIgFEHgazDxChHgj4werqiFM8CkbBKBgFIwoAABYeWRASHfCsAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-7547-847X","institution":"Aksaray University: Aksaray Universitesi","correspondingAuthor":true,"prefix":"","firstName":"Türker","middleName":"","lastName":"Tuğrul","suffix":""},{"id":268919344,"identity":"91b14d4d-65ae-4db8-a869-653e9e78bff3","order_by":1,"name":"Mehmet Ali HINIS","email":"","orcid":"","institution":"Aksaray University: Aksaray Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Mehmet","middleName":"Ali","lastName":"HINIS","suffix":""}],"badges":[],"createdAt":"2024-01-12 00:30:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3855107/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3855107/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11600-024-01399-z","type":"published","date":"2024-07-01T00:29:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59446985,"identity":"801be472-8067-4f54-b57b-4f4b64aa4d10","added_by":"auto","created_at":"2024-07-02 00:29:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1388548,"visible":true,"origin":"","legend":"","description":"","filename":"JournalarticleTurkertugrul.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3855107/v1_covered_674ff7f9-ea38-48e3-9359-6174223d1d4f.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eImprovement of Forecasting Drought by Means of Various Machine Learning Algorithms and Wavelet Transformation\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"acta-geophysica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agph","sideBox":"Learn more about [Acta Geophysica](http://link.springer.com/journal/11600)","snPcode":"11600","submissionUrl":"https://www.editorialmanager.com/agph/default2.aspx","title":"Acta Geophysica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"drought, drought modelling, SPI, wavelet transform, dam management, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-3855107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3855107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDefined as a decrease in average rainfall amounts, drought is one of the most insidious natural disasters. When it starts, people may not be aware of it, that's why droughts are difficult to monitor. Scientists have long been working to predict and monitor droughts. For this purpose, they have developed many methods such as drought indices one of which Standardized Precipitation Index (SPI) is. In this study, SPI to detect droughts and machine learning algorithms, support vector machines (SVM), artificial neural networks (ANN), random forest (RF), decision tree (DT), frequently used in the literature to predict droughts and 3 different statistical methods: correlation coefficient (r), Root Mean-square Error (RMSE), Nash-Sutcliffe Efficiency (NSE) Coefficient to investigate model performance values were used. Wavelet analysis was also applied to improve model performances. Konya closed basin located in the middle of Türkiye in terms of location and is among the leading regions of Turkey in terms of grain is one of the regions most affected by droughts in Türkiye. One of the most important water resources of the region is the Apa dam. It provides water to many fields which fertile land in its vicinity and is affected by droughts. Therefore, this region was determined as the study area. Meteorological data, total monthly precipitation, that could represent the region were obtained between 1955 and 2020 from general directorate of state water works and general directorate of meteorology. The results show that among the models analyzed with machine learning algorithms, the best results were obtained from M04 model whose input structure was created from SPI, different times steps, data delayed up to 5 months and total monthly precipitation data for time t-1. Among machine learning algorithms, SVM has achieved the most successful results in not only without wavelet transform (WT) but also with WT. Effective results were obtained from M04 in which SVM with WT was used (NSE = 0.9942, RMSE = 0.0764, R = 0.9971).\u003c/p\u003e","manuscriptTitle":"Improvement of Forecasting Drought by Means of Various Machine Learning Algorithms and Wavelet Transformation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 17:03:17","doi":"10.21203/rs.3.rs-3855107/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-01-23T16:10:01+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-23T08:47:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Acta Geophysica","date":"2024-01-21T21:32:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-17T15:57:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Acta Geophysica","date":"2024-01-10T17:12:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"acta-geophysica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agph","sideBox":"Learn more about [Acta Geophysica](http://link.springer.com/journal/11600)","snPcode":"11600","submissionUrl":"https://www.editorialmanager.com/agph/default2.aspx","title":"Acta Geophysica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b47eb149-e5f1-486f-bdc1-1aea255d5089","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-02T00:29:40+00:00","versionOfRecord":{"articleIdentity":"rs-3855107","link":"https://doi.org/10.1007/s11600-024-01399-z","journal":{"identity":"acta-geophysica","isVorOnly":false,"title":"Acta Geophysica"},"publishedOn":"2024-07-01 00:29:40","publishedOnDateReadable":"July 1st, 2024"},"versionCreatedAt":"2024-01-25 17:03:17","video":"","vorDoi":"10.1007/s11600-024-01399-z","vorDoiUrl":"https://doi.org/10.1007/s11600-024-01399-z","workflowStages":[]},"version":"v1","identity":"rs-3855107","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3855107","identity":"rs-3855107","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}