GeoAI & Machine Learning for Hazard Risk Assessment

Abstract

ABSTRACT Hazards such as earthquakes, floods, landslides, and wildfires pose significant threats to human life, infrastructure, and economic stability. Traditional hazard risk assessment methods often struggle with dynamic changes and large volumes of heterogeneous data. Recent advancements in GeoAI (Geospatial Artificial Intelligence) and Machine Learning (ML) offer promising tools for improving the accuracy and timeliness of hazard risk assessments. This paper reviews the current applications of GeoAI and ML in hazard risk assessment, discusses different techniques, and highlights the opportunities and limitations of these technologies. Specifically, machine learning models such as Random Forests, Support Vector Machines, Neural Networks, and Deep Learning integrated with geospatial data provide enhanced predictive capabilities. We also emphasize data challenges, the need for interdisciplinary collaboration, and future directions for research in GeoAI for hazards. The objective is to give an accessible overview that could help both practitioners and decision-makers understand the potential and constraints of these tools.

KEYWORDS: GeoAI, Machine Learning, Hazard Risk Assessment, Geospatial Data, Predictive Modeling, Natural Hazards, Deep Learning, Uncertainty, Remote Sensing.