Development of Strategies for Flood Risk Management

Classical computing struggles with the complexity of large-scale, high-dimensional data and unbalanced datasets for accurate flood prediction. Traditional methods show limitations in predicting flood occurrences.

Quantum machine learning (QML) algorithms like QSVM and QCNN can process complex climate and hydrological data more effectively. They demonstrate higher sensitivity in predicting floods.

Improved and more accurate flood predictions can enhance disaster prevention and management strategies, increasing climate resilience in water systems.