Forecasting Floods

Traditional computing faces challenges with the large, complex data needed for accurate flood forecasting. Classical methods can struggle with prediction sensitivity in such scenarios.

Quantum Machine Learning (QML) algorithms, like Quantum Support Vector Machines (QSVM) and Quantum Convolutional Neural Networks (QCNN), can more effectively analyze extensive hydrological data. QCNN excels at identifying complex patterns.

More accurate and timely flood predictions with improved sensitivity can be achieved. This leads to better disaster prevention and enhanced resilience of water systems.