Flood Prediction Using Classical and Quantum Machine Learning Models

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.