An integrated geographic information system (GIS) and analytical hierarchy process (AHP)-based approach for drone-optimized large-scale flood imaging

Drones are a valuable tool in flood response, providing high-resolution data and real-time monitoring capabilities. However, their limited range, swath, and battery life make it challenging to cover extensive flood-prone areas. This study addresses these limitations by introducing the drone-optimized flood risk map (DOFRM) framework, integrating drones with geographic information systems (GIS) and multi-criteria decision model to prioritize survey areas. The approach leverages analytical hierarchy process (AHP) to rank high-priority zones for effective drone survey and disaster response. The study evaluated 178 drones to identify an optimal survey grid size of 1.2 x 1.2 km for efficient drone operation. This grid was placed over a flood risk map, which is a combination of various hazard and vulnerability factors, with each factor given a weight based on AHP criteria. DOFRM revealed that 17% of the region was highly susceptible to flooding. This high-risk area was further divided based on the critical regions: urban areas (3%), active channels (5%), roads (6%), rail networks (1%), stream networks (3%), and populated areas (9%). DOFRM was perceived effective and easier to use through technology acceptance model-based stakeholder's survey. The framework enables prioritized drone deployment during large-scale flood events by optimizing resources for rapid assessment of vulnerable areas. By combining AHP-based prioritization with a GIS-based drone-optimized grid, the approach offers a systematic solution for flood risk mapping and disaster mitigation. This innovative framework enhances targeted flood surveys, enabling drone operations more effective and responsive to surveying needs.