Flood susceptibility assessment using artificial neural networks in Indonesia

Flood incidents can massively damage and disrupt a city economic or governing core. However, fl ood risk can be mitigated through event planning and city-wide preparation to reduce damage. For, governments, fi rms, and civilians to make such preparations, fl ood susceptibility predictions are required. To predict fl ood susceptibility nine environmental related factors have been identi fi ed. They are elevation, slope, curvature, topographical wetness index (TWI), Euclidean distance from a river, land-cover, stream power index (SPI), soil type and precipitation. This work will use these environmental related factors alongside Sentinel-1 satellite imagery in a model intercomparison study to back-predict fl ood susceptibility in Jakarta for the January 2020 historic fl ood event across 260 key locations. For each location, this study uses current environmental conditions to predict fl ood status in the following month. Considering the imbalance between instances of fl ooded and non-fl ooded conditions, the Synthetic Minority Oversampling Technique (SMOTE) has been implemented to balance both classes in the training set. This work compares predictions from arti fi cial neural networks (ANN), k-Nearest Neighbors algorithms (k-NN) and Support Vector Machines (SVM) against a random baseline. The effects of the SMOTE are also assessed by training each model on balanced and imbalanced datasets. The ANN is found to be superior to the other machine learning models.