Spatial-Temporal Flood Hazard Mapping Using Integration of Telemetry Data and Prediction Model

Abstract

The flood early warning system can help mitigate the resulting damages by predicting future events. This is achieved through the utilization of data obtained from telemetry stations to predict the values of water levels in the future. Flood hazard maps are considered a tool for representing the potential flood events that occur in an area. This paper proposed a framework to apply the spatial and temporal data to generate flood hazard mapping using the integration of interpolation telemetry station data and a temporal prediction model. The framework consists of two components: 1) the temporal prediction model is applied to water level prediction on hourly and daily scales, and 2) the interpolation of spatial data to generate a flood hazard map. The evaluation results show that the hourly and daily temporal prediction models can predict the water level with an average of MAPE using 500 iterations are 3.17% and 4.88% of training, and 3.48% and 4.72% of testing. Then, the flood hazard map is generated. The accuracy is 70.90% and F1-score is 81.50% compared to the observation flood event.