Technical Information Technology and Telecommunications, Engineering Professors Council, England
Mini Review
Flood Prediction Using Classical and Quantum Machine Learning Models.
Author(s): Marek Grzesiak* and Param Thakkar
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_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 cli- mate change adaptation. We emphasize collaboration and continuous innovation to implement this model in real-world flood management, ultimately enhancing global resili.. Read More»