  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
