EXPLAINABLE DEEP LEARNING FOR INTELLIGENT GEO DISASTER DETECTION AND RESILIENCE PLANNING

Abstract

This research employs explainable deep learning to identify clever geo-disasters and plan for resilience. We need data-driven solutions to help identify disasters more quickly and manage them more effectively as the frequency and intensity of earthquakes, landslides, floods, and wildfires increase. In order to identify patterns associated with likely geo-disasters, deep learning algorithms can rapidly examine geographic data from satellite photos, remote sensing systems, environmental sensors, and records of previous disasters. Despite this, decision-makers struggle to comprehend their estimations due to the ambiguity of conventional deep learning models. Explainable deep learning employs techniques for attribute attribution, visualization, and attention to make model predictions more understandable. Explainable deep learning improves early warning systems, resilience planning, and crisis risk assessment by making prediction models more transparent and trustworthy. Lastly, geospatial analytics and explainable AI can assist researchers, governments, and emergency management organizations in making informed decisions that reduce risks, safeguard infrastructure, and improve communities.