Leveraging quantum machine learning for early warning systems in sudden environmental disaster prediction

The frequency of sudden environmental catastrophes like floods, wildfires and hurricanes, among others indicates the essence of a better early warning system that can provide a better forecast. Peculiarities of traditional models are their inability to handle high-dimensional environmental data and changes in a real-time environment. Based on this research, the application of QML to improve the prediction accuracy and reliability of disaster early warning systems is suggested. Quantum support vector machine and quantum neural network are used with real-time environmental data to enhance prediction in the case of disasters. The approach blends in with modern quantum algorithms. Specifically, DEA is used along with quantum optimisation to enhance feature selection and model training, unlike conventional methods. The framework is verified and validated by employing benchmark datasets, QM9 and PDBbind, to obtain important information about atmospheric conditions, temperature and soil moisture. The findings show that the proposed quantum machine learning models calculate predictions more accurately and efficiently than traditional ML models. The results suggest that quantum computing could change disaster prediction systems and the ways of reducing the consequences of environmental catastrophes. This research offers an important background for introducing quantum technologies for environmental and disaster detection services.