Performance benchmarking on several regression models applied in urban flash flood risk assessment

Performance benchmarking on several regression models applied in urban flash flood risk assessment | 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 Performance benchmarking on several regression models applied in urban flash flood risk assessment Haibo Hu, Miao Yu, Xiya Zhang, Ying Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2897923/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Dec, 2023 Read the published version in Natural Hazards → Version 1 posted 4 You are reading this latest preprint version Abstract To evaluate the performances of regression models applied in the urban flash flood risk assessment (UFFRA), the historical urban flash flood occurrences points were used to build the Voronio polygon networks for calculating Repley’s K values which can be adopted to be the risk value and the predictands in regression. The first level risk indicators of hazard, vulnerability, sensitivity and exposure risk factors in the risk assessment, as well as the sensitivity subordinate indicators of imperviousness and terrain factor, were listed to be the predictors in the regression model. Subsequently, methods of the linear regression equation (LRE), nonlinear regression power-form function (PF) and a simplified power-form function (SPF), as well as Support Vector Machine (SVM) model and Random Forests (RF) model, were all nominated for the performance evaluation and comparison of the fitness of their regression relationships between the predictors and the predictands. With the support of samples, the benchmarking firstly demonstrated the SPF is the best of the regression equation; but the full PF equation can’t be figured out on account of the sample data deficiency. The SVM model behaves better than the regression equations of SPE and LRE, while the SVM of nonlinear Polynomial kernel function (PKF) is slightly better than that of the nonlinear Gussian kernel function (GKF). Above all, the RF model performed perfectly in the regression fitting, which the relative bias (BIASr) index is -0.009 and the relative mean squared error (RMSEr) is 0.0773. Meanwhile, it mostly resolves the problems of overfitting, outliers and noise in regression. The variable importance (VI) evaluated by the RF model indicated that the top four important risk factors are the imperviousness, terrain factor, vulnerability, and exposure factor, which the VI index value is 0.38, 0.16, 0.11 and 0.1, respectively. Unexpectedly, the hazard factor appears to be the least important factor with a VI value of 0.04. The homogeneity of invariable hazard being preserved in regional climate background makes the hazard a minor role in risk contribution. The model performance evaluation demonstrated the artificial intelligence (AI) RF model should be recommended to be the common-use model for aftermath meteorology related risk assessment. On the other hand, the VI analysis tools of RF were also recognized to be a welcome toolbox items for the risk analysis. Flash flood Risk assessment Random forests Full Text Cite Share Download PDF Status: Published Journal Publication published 07 Dec, 2023 Read the published version in Natural Hazards → Version 1 posted Reviewers agreed at journal 22 Jun, 2023 Reviewers invited by journal 07 May, 2023 Editor assigned by journal 06 May, 2023 First submitted to journal 05 May, 2023 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. 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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-2897923","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":198083512,"identity":"16b624ae-5241-4b92-acba-bede4d764d35","order_by":0,"name":"Haibo Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAf0lEQVRIiWNgGAWjYBACAwkeBoYPBqRqYZxBshZmHpIcZi7de0zapuBwNAP72QPEabGccy5NOsfgcG4DT14CkQ67kWMG0SLBQ6SPwFosSNfCQJqWO2eMLXsM0nPbeHKI1XK7x/DGjz/Wuf3sZ0iKHSBgI1H9KBgFo2AUjAJ8AAB7ESXC8Zy8OAAAAABJRU5ErkJggg==","orcid":"","institution":"Chinese Academy of Meteorological Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Haibo","middleName":"","lastName":"Hu","suffix":""},{"id":198083513,"identity":"86b81057-82b1-4282-91d7-8e619e5b1b2c","order_by":1,"name":"Miao Yu","email":"","orcid":"","institution":"Chinese Academy of Meteorological Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Yu","suffix":""},{"id":198083514,"identity":"c34282fe-f2c0-4c1b-8a8b-4480b0f05e90","order_by":2,"name":"Xiya Zhang","email":"","orcid":"","institution":"Institute of Urban Meteorology, CMA","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiya","middleName":"","lastName":"Zhang","suffix":""},{"id":198083515,"identity":"dea8f85b-3ef9-4455-81d8-b98dbccc8b6e","order_by":3,"name":"Ying Wang","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-05-05 11:36:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2897923/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2897923/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11069-023-06341-y","type":"published","date":"2023-12-07T15:00:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":47988743,"identity":"bbd926f6-1772-468b-8b6a-46c1d7558361","added_by":"auto","created_at":"2023-12-11 15:05:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1452239,"visible":true,"origin":"","legend":"","description":"","filename":"Performancebenchmarking.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2897923/v1_covered_2ebc7eab-8619-4e3a-93c9-5f4a1a1c8adb.pdf"}],"financialInterests":"","formattedTitle":"Performance benchmarking on several regression models applied in urban flash flood risk assessment","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":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Flash flood, Risk assessment, Random forests","lastPublishedDoi":"10.21203/rs.3.rs-2897923/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2897923/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"To evaluate the performances of regression models applied in the urban flash flood risk assessment (UFFRA), the historical urban flash flood occurrences points were used to build the Voronio polygon networks for calculating Repley’s K values which can be adopted to be the risk value and the predictands in regression. The first level risk indicators of hazard, vulnerability, sensitivity and exposure risk factors in the risk assessment, as well as the sensitivity subordinate indicators of imperviousness and terrain factor, were listed to be the predictors in the regression model. Subsequently, methods of the linear regression equation (LRE), nonlinear regression power-form function (PF) and a simplified power-form function (SPF), as well as Support Vector Machine (SVM) model and Random Forests (RF) model, were all nominated for the performance evaluation and comparison of the fitness of their regression relationships between the predictors and the predictands. With the support of samples, the benchmarking firstly demonstrated the SPF is the best of the regression equation; but the full PF equation can’t be figured out on account of the sample data deficiency. The SVM model behaves better than the regression equations of SPE and LRE, while the SVM of nonlinear Polynomial kernel function (PKF) is slightly better than that of the nonlinear Gussian kernel function (GKF). Above all, the RF model performed perfectly in the regression fitting, which the relative bias (BIASr) index is -0.009 and the relative mean squared error (RMSEr) is 0.0773. Meanwhile, it mostly resolves the problems of overfitting, outliers and noise in regression. The variable importance (VI) evaluated by the RF model indicated that the top four important risk factors are the imperviousness, terrain factor, vulnerability, and exposure factor, which the VI index value is 0.38, 0.16, 0.11 and 0.1, respectively. Unexpectedly, the hazard factor appears to be the least important factor with a VI value of 0.04. The homogeneity of invariable hazard being preserved in regional climate background makes the hazard a minor role in risk contribution. The model performance evaluation demonstrated the artificial intelligence (AI) RF model should be recommended to be the common-use model for aftermath meteorology related risk assessment. 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