Flood Prediction Using Classical and Quantum Machine Learning Models
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This study compared classical machine learning models with quantum machine learning models for predicting daily flood events along Germany's Wupper River, finding QML models offered improved accuracy and competitive training times.
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This preprint investigates whether quantum machine learning (QML) can improve flood forecasting for daily flood events along Germany’s Wupper River in 2023 by comparing classical models (SVM, KNN, regression, AR models) with QML methods (Adaboost, Quantum Variational Circuits, QBoost, QSV C_M L) using metrics including training time, accuracy, and scalability. The authors report that QML models achieved competitive training times and improved prediction accuracy relative to classical approaches. A major caveat is that the work is a preprint and not peer reviewed, and the abstract does not specify detailed dataset size, validation strategy, or statistical robustness. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
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Abstract
Abstract This study investigates the potential of quantum machine learning (QML) to improve flood forecasting. We focus on daily flood events along Germany’s Wupper River in 2023. Our approach combines classical machine learning (SVM, KNN, regression, AR models) with QML techniques (Adaboost, Quantum Variational Circuits, QBoost, QSV C_M L). This hybrid model leverages quantum properties like superposition and entanglement to achieve better accuracy and efficiency. Classical and QML models are compared based on training time, accuracy, and scalability. Results show that QML models offer competitive training times and improved prediction accuracy. This research signifies a step towards utilizing quantum technologies for climate change adaptation. We emphasize collaboration and continuous innovation to implement this model in real-world flood management, ultimately enhancing global resilience against floods.
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Flood Prediction Using Classical and Quantum Machine Learning Models | 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 Systematic Review Flood Prediction Using Classical and Quantum Machine Learning Models Marek Grzesiak, Param Thakkar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4593688/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 study investigates the potential of quantum machine learning (QML) to improve flood forecasting. We focus on daily flood events along Germany’s Wupper River in 2023. Our approach combines classical machine learning (SVM, KNN, regression, AR models) with QML techniques (Adaboost, Quantum Variational Circuits, QBoost, QSV C_M L). This hybrid model leverages quantum properties like superposition and entanglement to achieve better accuracy and efficiency. Classical and QML models are compared based on training time, accuracy, and scalability. Results show that QML models offer competitive training times and improved prediction accuracy. This research signifies a step towards utilizing quantum technologies for climate change adaptation. We emphasize collaboration and continuous innovation to implement this model in real-world flood management, ultimately enhancing global resilience against floods. Full Text Additional Declarations No competing interests reported. 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. 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