ASPIRE: UAV-Based Flood and Rainfall Scenario Prediction Using WebODM and QGIS in Barangay Bancal, Guagua, Pampanga, PHILIPPINES

Flooding is among the most devastating natural hazards in the Philippines, especially in low-lying communities like Guagua in Pampanga, where traditional flood monitoring systems typically fail to deliver the spatial resolution and timeliness needed for efficient disaster risk management. This study created and assessed a UAV-based flood inundation modelling framework that integrates open-source photogrammetry and GIS technologies to facilitate local flood hazard assessment. The study location was Barangay Bancal, a flood-prone area affected by the Pampanga River and the Pasac Delta, suggested by the Municipal Disaster Risk Reduction and Management Office. Based on performance, propulsion-to-weight ratio and flight stability concerns, a custom-constructed hexacopter UAV was designed for high-resolution aerial imagery acquisition. The resulting pictures were processed in WebODM to generate orthomosaic maps and Digital Surface Models with a Ground Sampling Distance of ≤5 cm/pixel. Generated geospatial products were analysed in QGIS to model flood inundation under three rainfall scenarios, illustrating low (40.85 mm), moderate (65.00 mm) and high (279.55 mm) rainfall events. This framework was used to determine 12 places in Barangay Bancal, and color-coded flood hazard maps with low, moderate, high, and severe risk categories were produced. The anticipated flood depths at Sitio Fatima were found to be the greatest among the simulation results, with values of 0.919 m, 0.987 m and 0.987 m for the relevant rainfall scenarios. Validation against the historical flood data resulted in a maximum location-specific accuracy of 96.68% in Sitio Lawiswis, while the entire model had a prediction accuracy of 79.17% with a mean percentage error of 20.83%, showing an acceptable agreement with the observed flood conditions. The created framework provides an accurate, low-cost and high-resolution flood hazard mapping approach, which can be used by local government entities to improve flood preparedness, evacuation planning and catastrophe risk reduction.