[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118446-en":3,"doc-seo-118446-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118446,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Flood Prediction Using Classical and Quantum Machine Learning Models","This study investigates how quantum machine learning (QML) can improve flood forecasting. The research targets daily flood events on Germany’s Wupper River in 2023 and builds a hybrid framework that integrates classical methods—SVM, KNN, regression, and autoregressive models—with QML approaches such as Adaboost, Quantum Variational Circuits, QBoost, and QSVC_ML. Models are evaluated by training time, prediction accuracy, and scalability. Results show competitive training times alongside improved prediction accuracy, indicating a practical direction for quantum-enabled climate adaptation and more resilient flood management.","arXiv :2407 .0 100 1v 1 [ cs .LG] 1 Jul 2024  \nFlood Prediction Using Classical and  \nQuantum Machine Learning Models Marek Grzesiak *1 and Param Thakkar2  \n1 Ekipa, Dusseldorf, Germany, AGH University of Science and Technology, Krakow, Poland, Engineering Professors  \nCouncil, England  \n2 Veermata Jijabai Technological Institute, Mumbai, India  \n*[marekgrzesiak.22@gmail. com](marekgrzesiak.22@gmail. com)  \nAbstract  \nThis 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, QSVC_ML) . 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.  \n1 Introduction  \nFlooding is a major natural disaster affecting millions worldwide, and its prediction remains a significant challenge. Accurate flood forecasting is essential for mitigating the adverse effects on human lives and infrastructure. This project investigates the application of Quantum Machine Learning (QML) to enhance flood prediction accuracy and efficiency, specifically focusing on the Wupper River in Germany during 2023 .  \nTraditional flood prediction models rely on classical machine learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), regression, and Autoregressive (AR) models. While effective, these methods face limitations in handling large datasets and complex patterns inherent in environmental data. QML offers a promising alternative by exploiting quantum phenomena like superposition and entanglement, which enable the processing of vast amounts of data at unprecedented speeds.  \nOur approach integrates classical and quantum models, including SVM, KNN, Adaboost, Quantum Variational Circuits, QBoost, and QSVC_ML, to develop a hybrid system for flood prediction. By comparing the performance of classical and QML models based on training time, accuracy, and scalability, we aim to demonstrate the superiority of QMLin handling intricate flood prediction tasks.  \nThe results indicate that QML models not only enhance prediction accuracy but also reduce computational time, making them a viable option for real-time flood forecasting. This research underscores the potential of quantum technologies in addressing climate-related challenges and highlights the importance of interdisciplinary collaboration in advancing environmental science.  \n2 Model Descriptions  \nIn this section, we describe the various models used in our study, including both classical and quantum machine learning techniques, and explain how they were applied to flood predictions.  \n2.1 Classical Machine Learning Models  \n2.1.1 Support Vector Machines (SVM)  \nSVM is a supervised learning model used for binary classification tasks. In flood prediction, SVM was employed to classify the likelihood of flooding events based on historical and real-time data. The model works by finding the optimal hyperplane that separates the data into different classes, indicating whether a flood is likely to occur or not. SVMs are effective in high-dimensional spaces and are versatile due to the different kernel functions that can be used to customize the decision boundary.  \n2.1.2 K-Nearest Neighbors (KNN)  \nKNN is a simple yet powerful supervised learning algorithm used for both classification and regression tasks. In our study,","cbCaicrX7MbLq1LY","https://ap.wps.com/l/cbCaicrX7MbLq1LY","pdf",646261,1,24,"English","en",105,"# Abstract\n# Introduction\n# Model Descriptions\n## Classical Machine Learning Models\n## Quantum Machine Learning Models","[{\"question\":\"What is the main goal of the study on flood prediction?\",\"answer\":\"The study aims to improve flood forecasting accuracy and efficiency by applying quantum machine learning alongside classical machine learning models.\"},{\"question\":\"Which classical models are used for flood prediction?\",\"answer\":\"The classical models include SVM, KNN, linear regression, and autoregressive (AR) time series models.\"},{\"question\":\"What quantum machine learning techniques are integrated in the hybrid approach?\",\"answer\":\"The QML components include Adaboost with quantum-enhanced decision stumps, Quantum Variational Circuits, QBoost, and QSVC_ML.\"}]","Flood Prediction Using Classical and Quantum Machine Learning Models | PDF",1785683652,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"flood-prediction-using-classical-and-quantum-machine-learning-models","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/flood-prediction-using-classical-and-quantum-machine-learning-models/118446/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study on flood prediction?","Question",{"text":75,"@type":76},"The study aims to improve flood forecasting accuracy and efficiency by applying quantum machine learning alongside classical machine learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which classical models are used for flood prediction?",{"text":80,"@type":76},"The classical models include SVM, KNN, linear regression, and autoregressive (AR) time series models.",{"name":82,"@type":73,"acceptedAnswer":83},"What quantum machine learning techniques are integrated in the hybrid approach?",{"text":84,"@type":76},"The QML components include Adaboost with quantum-enhanced decision stumps, Quantum Variational Circuits, QBoost, and QSVC_ML.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]