Appraisal of Flood Prone Area Management Using Artificial Intelligence Methods in Jakarta Basin, Indonesia

Abstract

Jakarta often experiences floods every rainy season. Some major floods that crippled human activities have occurred in 2002, 2007, 2013, and 2020. The factors affecting the floods are the lowland basin and land subsidence of Jakarta. The analysis used in this study is geographic information systems (GIS) tools with artificial intelligence (AI) methods to produce flood distribution models. Also, hydrogeochemical analysis is conducted to determine seawater intrusion and its correlation with land subsidence that causes floods in Jakarta. The AI methods show that the Genetic Algorithm Rule-set Production, GARP (AUC-ROC = 0.90) has a greater value than the Quick Unbiased Statistical Tree, QUEST (AUC-ROC = 0,79). The results show that GARP is the best method to produce the model distribution of flood hazard points which has been dominating in Northern Jakarta. The correlation between the results of the flood distribution model and the seawater intrusion shows that the condition of land subsidence rate in Jakarta is very massive. The output of this research serves as the basis for determining a better spatial plan for Jakarta in the future.