Flood Prediction Using Classical and Quantum Machine Learning Models

TL;DR

This paper explores the integration of classical and quantum machine learning models to enhance flood prediction accuracy and efficiency, demonstrating promising results for climate change adaptation and flood management.

Contribution

It introduces a hybrid classical-quantum machine learning approach for flood forecasting, highlighting quantum advantages in accuracy and scalability.

Findings

QML models achieve better prediction accuracy

QML models have competitive training times

Hybrid models improve flood forecasting efficiency

Abstract

This study investigates the potential of quantum machine learning to improve flood forecasting we focus on daily flood events along Germany's Wupper River in 2023 our approach combines classical machine learning techniques with QML techniques 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