Enhancing Urban Resilience to Flooding in Hydrogeological Risk Areas Through Big Data Analytics Using Deep Neuro-Fuzzy System

Enhancing Urban Resilience to Flooding in Hydrogeological Risk Areas Through Big Data Analytics Using Deep Neuro-Fuzzy System | 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 Article Enhancing Urban Resilience to Flooding in Hydrogeological Risk Areas Through Big Data Analytics Using Deep Neuro-Fuzzy System Varun Malik, R. John Martin, Ruchi Mittal, Ravula Sahithya Ravali, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4615497/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 Urban areas worldwide are increasingly at risk from hydrogeological hazards, leading to severe consequences. Urban flooding and mismanagement of water resources, resulting in riverine flooding, are primary contributors to this risk. Utilizing big data, including mobile phone signals collected at high frequencies, alongside administrative data, is essential for developing risk exposure indicators in smaller urban regions. Accurately assessing human traffic flows and movements is crucial for mitigating the impacts of natural disasters and ensuring a high quality of life in smart cities. However, comprehensive solutions to these challenges are lacking in many countries. Therefore, this study focuses on analyzing the impact of traffic data flow analysis in hydrogeological risk areas. The study employs mobile phone signals as big data to analyze traffic flows and forecast exposure risks to aid decision-making. To ensure data reliability, a circle search integrated fully connected conditional neural network (CS-ConNN) is used for data cleaning, categorizing mobile phone signal data into normal, empty, and garbage. Additionally, the study uses a deep recurrent neuro fuzzy system (DRNFS) to analyze the compound seasonality of circulation flow data and forecast risks, providing alerts to individuals transiting through affected areas. The model is validated through a case study of "Mandolossa," and developed area prone to inundating near Brescia, using hourly data from September 2020 to August 2021. Experimental results and cross-validation demonstrate a forecasting accuracy of 98.975%. Earth and environmental sciences/Hydrology Physical sciences/Mathematics and computing deep neuro fuzzy system data cleaning big data driven decision making data flow optimization hydrogeological risk hyperparameter tuning Full Text Additional Declarations No competing interests reported. Supplementary Files DataGeneratedAnalysed.xlsx 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-4615497","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":328269973,"identity":"d77bd495-cbd4-4866-bbad-22b17ccf8cf6","order_by":0,"name":"Varun Malik","email":"","orcid":"","institution":"Chitkara University Institute of Engineering and Technology, Chitkara University","correspondingAuthor":false,"prefix":"","firstName":"Varun","middleName":"","lastName":"Malik","suffix":""},{"id":328269974,"identity":"c5d2fe08-e3ae-476b-b624-d572f0d6b4f3","order_by":1,"name":"R. John Martin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACxgYQacAgB2GAwAFitBwwYDAmXgtMUWIDMg8vYJ52+NnnDwV30pvbjz/8XNjGIMd3I4H5ww98DpudZjzjgMGz3MaehGTpmW0MxpI3Etgke/BqSTAG+uVwbmNDwgFp3jaGxA1ALQw8eLWkfwZpSWfsf9j8G6ilHqiF+eMfvFpywLYkMM5IZgPZkmBwI4FBGr8tOcUMZwwOGzbOeMZmzXNOwnDmmYdt0jJ4tBjOTt/MUPHnsLxhf/rj2zxlNvJ8x5MPf3yDT0sDKkOCAZYicAJ5DMYoGAWjYBSMAnQAAFdbUZIjVJsJAAAAAElFTkSuQmCC","orcid":"","institution":"Jazan University","correspondingAuthor":true,"prefix":"","firstName":"R.","middleName":"John","lastName":"Martin","suffix":""},{"id":328269975,"identity":"45634a8a-f467-4cd0-9674-da3d3b568955","order_by":2,"name":"Ruchi Mittal","email":"","orcid":"","institution":"Chitkara University Institute of Engineering and Technology, Chitkara University","correspondingAuthor":false,"prefix":"","firstName":"Ruchi","middleName":"","lastName":"Mittal","suffix":""},{"id":328269976,"identity":"c8428ba7-4a35-4e96-bb79-502d0bd00968","order_by":3,"name":"Ravula Sahithya Ravali","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Ravula","middleName":"Sahithya","lastName":"Ravali","suffix":""},{"id":328269977,"identity":"78de89d1-7ba4-45f9-a172-3fb12717aced","order_by":4,"name":"Khalid Jaber Almalki","email":"","orcid":"","institution":"Saudi Electronic University","correspondingAuthor":false,"prefix":"","firstName":"Khalid","middleName":"Jaber","lastName":"Almalki","suffix":""},{"id":328269978,"identity":"fcf25a62-bb58-48c7-8af4-50c8ff068138","order_by":5,"name":"Jayabrabu Ramakrishnan","email":"","orcid":"","institution":"Jazan University","correspondingAuthor":false,"prefix":"","firstName":"Jayabrabu","middleName":"","lastName":"Ramakrishnan","suffix":""},{"id":328269979,"identity":"7a3c74de-f121-46ba-b37a-4f9e9cffac3c","order_by":6,"name":"SL Swapna","email":"","orcid":"","institution":"Applexus Technologies","correspondingAuthor":false,"prefix":"","firstName":"SL","middleName":"","lastName":"Swapna","suffix":""},{"id":328269980,"identity":"4e5eacb8-c376-4e5e-a3df-b8b7f08bc1a6","order_by":7,"name":"Dinesh Mavaluru","email":"","orcid":"","institution":"Saudi Electronic University","correspondingAuthor":false,"prefix":"","firstName":"Dinesh","middleName":"","lastName":"Mavaluru","suffix":""},{"id":328269981,"identity":"c9592972-dde1-4093-bc2a-92f5b395fb85","order_by":8,"name":"SB Goyal","email":"","orcid":"","institution":"City University","correspondingAuthor":false,"prefix":"","firstName":"SB","middleName":"","lastName":"Goyal","suffix":""},{"id":328269982,"identity":"70fe8464-b7b9-475c-b65e-48dbbdefcff4","order_by":9,"name":"Manoj Kumar","email":"","orcid":"","institution":"University of Wollongong","correspondingAuthor":false,"prefix":"","firstName":"Manoj","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2024-06-21 07:07:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4615497/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4615497/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70913431,"identity":"9184d58d-45bc-4964-a2b1-bf9beea9dede","added_by":"auto","created_at":"2024-12-09 07:40:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":729335,"visible":true,"origin":"","legend":"","description":"","filename":"MainManuscriptHydroGeo.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4615497/v1_covered_5ab6998d-8576-4ede-8117-35dcee8e2e87.pdf"},{"id":60904099,"identity":"c3e7c267-82a6-4c12-8f1e-77ed24a533db","added_by":"auto","created_at":"2024-07-23 11:28:43","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16483,"visible":true,"origin":"","legend":"","description":"","filename":"DataGeneratedAnalysed.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4615497/v1/80bb6fa28e81d28c584e4750.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Urban Resilience to Flooding in Hydrogeological Risk Areas Through Big Data Analytics Using Deep Neuro-Fuzzy System","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 Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"deep neuro fuzzy system, data cleaning, big data driven decision making, data flow optimization, hydrogeological risk, hyperparameter tuning","lastPublishedDoi":"10.21203/rs.3.rs-4615497/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4615497/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban areas worldwide are increasingly at risk from hydrogeological hazards, leading to severe consequences. Urban flooding and mismanagement of water resources, resulting in riverine flooding, are primary contributors to this risk. Utilizing big data, including mobile phone signals collected at high frequencies, alongside administrative data, is essential for developing risk exposure indicators in smaller urban regions. Accurately assessing human traffic flows and movements is crucial for mitigating the impacts of natural disasters and ensuring a high quality of life in smart cities. However, comprehensive solutions to these challenges are lacking in many countries. Therefore, this study focuses on analyzing the impact of traffic data flow analysis in hydrogeological risk areas. The study employs mobile phone signals as big data to analyze traffic flows and forecast exposure risks to aid decision-making. To ensure data reliability, a circle search integrated fully connected conditional neural network (CS-ConNN) is used for data cleaning, categorizing mobile phone signal data into normal, empty, and garbage. Additionally, the study uses a deep recurrent neuro fuzzy system (DRNFS) to analyze the compound seasonality of circulation flow data and forecast risks, providing alerts to individuals transiting through affected areas. The model is validated through a case study of \"Mandolossa,\" and developed area prone to inundating near Brescia, using hourly data from September 2020 to August 2021. Experimental results and cross-validation demonstrate a forecasting accuracy of 98.975%.\u003c/p\u003e","manuscriptTitle":"Enhancing Urban Resilience to Flooding in Hydrogeological Risk Areas Through Big Data Analytics Using Deep Neuro-Fuzzy System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 11:28:37","doi":"10.21203/rs.3.rs-4615497/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":"6250a899-1d88-4760-8730-506cfe99b807","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34744868,"name":"Earth and environmental sciences/Hydrology"},{"id":34744869,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2024-12-09T07:39:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-23 11:28:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4615497","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4615497","identity":"rs-4615497","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}