Deep Learning and AI-Powered Natural Catastrophes Warning Systems

Abstract

Natural catastrophes including hurricanes, floods, wildfires, and earthquakes can seriously harm people and property. Floods that destroy houses, businesses, government buildings, and other properties cause enormous economic losses in addition to human casualties. This loss cannot be recovered; however, flood damage can frequently be reduced by supporting suitable structural and non-structural solutions. Natural catastrophes have become more frequent and severe in recent years, primarily as a result of climate change. Due to the large number of small and low magnitude earthquakes, the hand-picked data used in manual approaches, and the possibility of some noisy disturbances in the background, the methods are not very dependable. As a result, automated techniques and algorithms are more effective when used for earthquake identification and detection. However, scientists and engineers can now more accurately and efficiently predict and avert natural disasters thanks to developments in machine learning and data analytics. By creating a deep learning model that can quickly determine an asset's structural status in the event of a seismic excitation, this study investigates the potential of artificial intelligence in various operational domains.