Flood Prediction with Artificial Intelligence An Exploratory Data Analysis Approach

Flood Prediction with Artificial Intelligence An Exploratory Data Analysis Approach | 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 Flood Prediction with Artificial Intelligence An Exploratory Data Analysis Approach Arya Vithal Mane, Rashmi Ravindra Halkarni, Pallavi Mahesh Bhat, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7063048/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 This paper presents the application of various ML and DL algorithms to de- termine occurrence of floods in India. In this study data driven methods are used which could help in predicting floods on a national and regional level. Using a real- world dataset containing environmental parameters (Minimum Temperature, Maximum Temperature, Minimum pH, Maximum pH, Rainfall level) and hydro- logical data (Reservoir level, Live capacity, Storage capacity, Current water level), the trends are analyzed, detect anomalies, and identify attributes influencing flood occurrence. A wide range of predictive models including ML techniques like Boot- strap Forest,, Support Vector Machine, Naive Bayes, KNN, ANFIS, and advanced DL models like ANN and LSTM networks. Boostrap Forest delivered an accuracy of 99.43was rejected due to its overfitting nature ,Hence LSTM Network was con- sidered the best and suitable model with an accuracy of 98.72%. Keywords: AI, RNN, ANFIS, GMDH, Flood prediction AI RNN ANFIS GMDH Flood prediction 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-7063048","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512995012,"identity":"1f7a35fb-9c0f-4cd3-8231-924d59b86af3","order_by":0,"name":"Arya Vithal 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In this study data driven methods are used which could help in predicting floods on a national and regional level. Using a real- world dataset containing environmental parameters (Minimum Temperature, Maximum Temperature, Minimum pH, Maximum pH, Rainfall level) and hydro- logical data (Reservoir level, Live capacity, Storage capacity, Current water level), the trends are analyzed, detect anomalies, and identify attributes influencing flood occurrence. A wide range of predictive models including ML techniques like Boot- strap Forest,, Support Vector Machine, Naive Bayes, KNN, ANFIS, and advanced DL models like ANN and LSTM networks. Boostrap Forest delivered an accuracy of 99.43was rejected due to its overfitting nature ,Hence LSTM Network was con- sidered the best and suitable model with an accuracy of 98.72%. 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