Bushfire prediction in Victoria, Australia using Generalized extreme value distribution

Bushfire prediction in Victoria, Australia using Generalized extreme value distribution | 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 Bushfire prediction in Victoria, Australia using Generalized extreme value distribution Anirban Khastagir, Iqbal Hossain, Nazneen Aktar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1471964/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 To facilitate appropriate design, management, and improvement of infrastructures for both rural and urban communities, the prediction for the occurrence of future extreme climatic events e.g. bushfire is intrinsic. Widely used extreme climatic event prediction method, Generalized Extreme Value (GEV), was used in this study in predicting annual peak Forest Fire Danger Index (FFDI), which is a measure of fire behaviour used in different parts of Australia. The GEV distribution was fitted for 17 selected stations spreading all around Victoria, Australia. The estimation of the parameters for the GEV distribution was performed using Maximum Likelihood Estimation (MLE) method. Three goodness of fit tests such as: Kolmogorov-Smirnov (KS) test, Anderson-Darling (AD) test, and Chi-square test result were used to verify the efficacy of GEV distribution. Fire frequency curves (FFCs) were derived for Victoria to identify fire prone regions. The study revealed that Fréchet (type II) extreme value distribution is suitable for modelling the annual peak of FFDI for most of the selected stations. Annual peak FFDI for the 100 years return level (time average of every 100 years) can vary from 34 (Dartmouth) i.e. high fire danger event to 146 (Walpeup) i.e. catstrophic bushfire event. The study noted that three stations in Victoria namely: Nhill, Walpeup and Ouyen are vulnerable to catastrophic fire danger situation (FFDI ≥100) with at least 1% probability of occurrence (1 in 100 year). The developed FFCs for Victoria will guide key public and private stakeholders namely: catchment management authorities, insurance companies, structural designers, traffic modellers, and forest hydrologists when designing and managing infrastructures in fire prone areas. Fire Frequency Curves (FFCs) parameter estimation methods GEV distribution goodness of fit tests Full Text 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-1471964","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":92680110,"identity":"228a04ee-6d5d-49af-a09c-343b805bf47b","order_by":0,"name":"Anirban Khastagir","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-0122-778X","institution":"RMIT University City Campus","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Anirban","middleName":"","lastName":"Khastagir","suffix":""},{"id":92680111,"identity":"57f9c513-4319-4aeb-983d-e18a88ddbf5c","order_by":1,"name":"Iqbal Hossain","email":"","orcid":"","institution":"Swinburne University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Iqbal","middleName":"","lastName":"Hossain","suffix":""},{"id":92680112,"identity":"b6372854-c878-4648-b47f-b0e7091d5ef5","order_by":2,"name":"Nazneen Aktar","email":"","orcid":"","institution":"RMIT University City Campus","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nazneen","middleName":"","lastName":"Aktar","suffix":""}],"badges":[],"createdAt":"2022-03-21 02:13:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1471964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1471964/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19540157,"identity":"472725dc-17f1-4f91-afc4-e4322f87132c","added_by":"auto","created_at":"2022-03-23 17:12:55","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":490601,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptTheoreticalandappliedclimatology.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1471964/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"Bushfire prediction in Victoria, Australia using Generalized extreme value distribution","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1471964/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"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":"Fire Frequency Curves (FFCs), parameter estimation methods, GEV distribution, goodness of fit tests","lastPublishedDoi":"10.21203/rs.3.rs-1471964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1471964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo facilitate appropriate design, management, and improvement of infrastructures for both rural and urban communities, the prediction for the occurrence of future extreme climatic events e.g. bushfire is intrinsic. Widely used extreme climatic event prediction method, Generalized Extreme Value (GEV), was used in this study in predicting annual peak Forest Fire Danger Index (FFDI), which is a measure of fire behaviour used in different parts of Australia. The GEV distribution was fitted for 17 selected stations spreading all around Victoria, Australia. The estimation of the parameters for the GEV distribution was performed using Maximum Likelihood Estimation (MLE) method. Three goodness of fit tests such as: Kolmogorov-Smirnov (KS) test, Anderson-Darling (AD) test, and Chi-square test result were used to verify the efficacy of GEV distribution. Fire frequency curves (FFCs) were derived for Victoria to identify fire prone regions. The study revealed that Fréchet (type II) extreme value distribution is suitable for modelling the annual peak of FFDI for most of the selected stations. Annual peak FFDI for the 100 years return level (time average of every 100 years) can vary from 34 (Dartmouth) i.e. high fire danger event to 146 (Walpeup) i.e. catstrophic bushfire event. The study noted that three stations in Victoria namely: Nhill, Walpeup and Ouyen are vulnerable to catastrophic fire danger situation (FFDI ≥100) with at least 1% probability of occurrence (1 in 100 year). The developed FFCs for Victoria will guide key public and private stakeholders namely: catchment management authorities, insurance companies, structural designers, traffic modellers, and forest hydrologists when designing and managing infrastructures in fire prone areas.\u003c/p\u003e","manuscriptTitle":"Bushfire prediction in Victoria, Australia using Generalized extreme value distribution","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-03-23 17:12:42","doi":"10.21203/rs.3.rs-1471964/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"224fbe6a-2e29-406f-9b44-fc381f0d2cb1","owner":[],"postedDate":"March 23rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-04-23T16:28:02+00:00","versionOfRecord":[],"versionCreatedAt":"2022-03-23 17:12:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1471964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1471964","identity":"rs-1471964","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}