A conceptual approach to predicting seismic events and flood risks using convolutional neural networks

This paper explores the application of convolutional neural networks (CNNs) in predictive modelling for seismic events and flood risks, with a particular focus on forecasting extreme quantile events that exceed historical data limits. This research enhances CNN architecture to improve accuracy in high quantile predictions by integrating multi-source spatiotemporal data, addressing a critical research gap. The methodology involves incorporating diverse datasets, including geospatial, meteorological, and historical seismic or flood records, into CNN models to augment predictive capabilities.

The development of real-time predictive capabilities requires robust computational infrastructures to manage large-scale data processing efficiently. Advanced CNN models must be tailored to represent complex hydrological processes with higher fidelity, enabling more accurate flood forecasts and better early warning systems. Additionally, interdisciplinary collaboration among governments, scientific institutions, and local stakeholders is critical in translating predictive advancements into effective disaster preparedness strategies. By refining CNN architectures and expanding their scope, flood prediction tools can provide earlier and more accurate warnings, strengthening community resilience.