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Abstract The increasing frequency of flood disasters around the globe highlights the need for creative approaches to improve disaster preparedness. This thorough analysis and assessment explore the topic of enhancing flood disaster resilience by utilising cutting-edge geo-computational techniques. By combining a variety of techniques, such as remote sensing, geographic information systems (GIS), LiDAR, unmanned aerial vehicles (UAVs), and cutting-edge technologies like machine learning and geospatial big data analytics, the study provides a complex framework for flood monitoring, risk assessment, and mitigation. By using remote sensing technology, flood occurrences can be tracked in real time and inundations may be precisely mapped, which makes proactive response plans possible. GIS facilitates effective evacuation planning by streamlining spatial analysis and decision-making procedures and providing critical insights into risky locations. High-resolution elevation data is provided by LiDAR technology, which is essential for precise flood modelling and simulation. Unmanned Aerial Vehicles (UAVs) may be quickly deployed to assist with situational awareness and damage assessment during a disaster. Furthermore, predictive skills are enhanced by the combination of machine learning and geographic big data analytics, opening the door to the creation of adaptive reaction plans and early warning systems. This investigation highlights how geo-computational tools may significantly improve community resilience and lessen the negative effects of flood disasters. After a thorough review of the literature and case studies, this study clarifies how these approaches might improve disaster response and preparation to a great extent.
Harshita Jain (Tue,) studied this question.