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Corresponding Author ORCID Identifer

Mahmoud Rehanhttps://orcid.org/0009-0001-9514-2962

Section

Regular Issue

Abstract

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. Traditional risk assessment methods often struggle to estimate such extremes, highlighting the need for more advanced predictive models capable of handling rare but high-impact events. 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. These models undergo systematic validation using historical events and real-world data to assess their reliability, robustness, and practical relevance. Furthermore, the study evaluates the potential of these advanced prediction models to inform disaster risk management and mitigation strategies. By leveraging deep learning techniques and optimizing CNN structures, this research aims to refine forecasting precision, supporting proactive disaster preparedness. The anticipated outcome is an improved predictive framework that enhances early warning systems, facilitates informed decision-making, and strengthens emergency response mechanisms. Ultimately, this study contributes to the broader goal of increasing resilience against natural disasters by equipping policymakers, emergency responders, and urban planners with more accurate and timely risk assessments.

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