Flood Risk Mapping of Jakarta Using Genetic Algorithm Rule-Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST) Methods

Abstract

Jakarta as the country’s capital, has experienced a series of floods that crippled cities in 2002, 2007 and 2013 and extreme rainfall has always been the main cause of major flood with casualties and property. Given the increasing impact of flooding in Jakarta, methods for assessing current and future flood risks is needed. A model is needed that can predict and determine which areas have the potential to be affected by future floods. Several models have been carried out for prediction and assessment of flood risk in Jakarta and several other major cities, namely GIS based on SVM models, hierarchical process analysis models (AHP), machine learning with ensemble models, and multivariate discriminant, classification, and regression trees. However, from several methods there are still some weaknesses, one of which is the time to get a predictive model that is quite long, which is 3-4 hours depending on the amount of data. To overcome this problem, genetic algorithm rule-set production (GARP) and quick unbiased efficient statistical tree (QUEST) models can create a prediction model with a shorter time with the same results. Several factors that influence flooding are used as input model data: precipitation, slope, distance to river, distance to channel, depth to groundwater, land use, elevation, and flood data for 2002 - 2019. The Area under receiver-operator characteristic curve (AUC-ROC) and root mean square error (RMSE) were used as evaluations for model performance. The results showed that the GARP model (AUC-ROC = 94%, RMSE = 0.2) had higher performance accuracy than the QUEST model (AUC-ROC = 84%, RMSE = 0.4). The results also indicated that the distance to channel, land use, and elevation as important parameters in determining flood hazards.