Cloud-Based Machine Learning for Flood Policy Recommendations in Makassar City, Indonesia

Abstract

Makassar City frequently experiences monsoonal floods, typical of a tropical city in Indonesia. However, there is no high-accuracy flood map for flood inundation. Examining the flood inundation area would help to provide a suitable flood policy. Hence, the study utilizes multiple satellite data sources on a cloud-based platform, integrating the physical factors of a flood (i.e., land use data and digital elevation model—DEM—data) with the local government’s urban land use plan and existing drainage networks. The research aims to map the inundation area, identify the most vulnerable land cover, slope, and elevation, and assess the efficiency of Makassar’s drainage system and urban land use plan. The study reveals that an uncoordinated drainage system in the Tamalanrea, Biringkanaya, and Manggala sub-districts results in severe flooding, encompassing a total area of 35.28 km2. The most affected land use type is cultivation land, constituting approximately 43.5% of the flooded area. Furthermore, 82.26% of the urban land use plan, covering 29.02 km2, is submerged. It is imperative for the local government and stakeholders to prioritize the enhancement of drainage systems and urban land use plans, particularly in low-lying and densely populated regions.