Identifying factors for supporting early warning flood using clustering approach and geo-spatial analysis
This research used ArcGIS Pro with unsupervised clustering method HDBSCAN to create a prototype that would make it easier for people to find areas prone to floods. The experiment received positive input from many respondents who had seen our prototype design and how it can assist people as an early warning of floods. Floods in Jakarta are very frequent and caused by many factors, from very high rainfall to flash floods. This natural disaster has greatly impact the society, from economy to health problems.
From the analysis results, the researchers can conclude that some areas of Jakarta are more prone to flood than the other parts as described. The two main factors that affect the floods are the rainfall intensity and the flood prone area. The flood-prone area is affected by high density population, distance from the drainage system to high density populated area, flood history, and rainfall intensity.