Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potential - Tahri Ayoub; Beroho Mohamed; Tichli Soufiane; El Talibi Hajar; El Moussaoui Said; Aboumaria Khadija (E3S Web of Conferences)
This review article aims to discuss the current status and future potential of Geographic Information Systems (GIS) in map-based technology of tsunami risk management, especially in seismically active, well-known tsunami regions of the world. It presents GIS technologies for hazard mapping, risk assessment, and information generation for disaster-response operations. These are important tools for accurately mapping vulnerable areas by integrating real-time and historical data to develop accurate forecasts for possible tsunamis. Demographic and geographic data were also analyzed by GIS to determine the optimum route to develop evacuation strategies. A set of case studies demonstrates how GIS improves community resilience by supporting informed decision-making. In addition, suggestions are made for how future steeps as the integration of machine learning techniques as emerging tools for analyzing and classifying complex and vast datasets, which may enhance GIS applications in tsunami risk management to improve the accuracy and utility of these tools.