Imputing Missing Precipitation Data at Benin Synoptic Stations (West Africa) by Using Machine Learning Methods

Imputing Missing Precipitation Data at Benin Synoptic Stations (West Africa) by Using Machine Learning Methods | 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 Imputing Missing Precipitation Data at Benin Synoptic Stations (West Africa) by Using Machine Learning Methods Mawinesso Gnonyi N’Kaina, Noukpo Médard Agbazo, Gabin Koto N’Gobi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7254774/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Precipitation is a key hydrometeorological variable in environmental, hydrological, and agroclimatic studies. Unfortunately, in developing countries as Benin Republic, daily precipitation records from synoptic stations are often characterized by substantial gaps. Thus, to obtain complete datasets, gap- filling is necessary. However, applying inappropriate gap-filling may lead to partial or biased results. This study aims to evaluate the capabilities of five Machine Learning Models (MLM) in estimating missing daily rainfall data over the period 1953-2010 across six Benin synoptic stations. The considered MLM are: (a) Decision Tree (DT), (b) Random Forest (RF), (c) Support Vector Machine (SVM), (d) Multi-Layer Perceptron (MLP), and (e) Multiple Linear Regression (MLR). Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are applied to evaluate their performances. The results show that the SVM and RF models generally outperformed the other models. But these performances varied unpredictably across stations and months. In contrast, the MLP and DT models showed poor performance. Moreover, all models generally performed better during dry months than the wet months. Finally, their performances was higher at synoptic stations located at higher latitudes. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. 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Methods","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 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Unfortunately, in developing countries as Benin Republic, daily precipitation records from synoptic stations are often characterized by substantial gaps. Thus, to obtain complete datasets, gap- filling is necessary. However, applying inappropriate gap-filling may lead to partial or biased results. This study aims to evaluate the capabilities of five Machine Learning Models (MLM) in estimating missing daily rainfall data over the period 1953-2010 across six Benin synoptic stations. The considered MLM are: (a) Decision Tree (DT), (b) Random Forest (RF), (c) Support Vector Machine (SVM), (d) Multi-Layer Perceptron (MLP), and (e) Multiple Linear Regression (MLR). Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are applied to evaluate their performances. The results show that the SVM and RF models generally outperformed the other models. But these performances varied unpredictably across stations and months. In contrast, the MLP and DT models showed poor performance. 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