Jakarta, the capital region of Indonesia, is experiencing recurring floods, with the most extensive recording loss as high as 350 million dollars. Katulampa Barrage’s observation of the Upper Ciliwung River plays a central role in reducing the risk of flooding in Jakarta, especially flowing through the Ciliwung River. The peak flow measured in the barrage would travel 13–14 h to the heart of the city, providing adequate time for the government officials and the residents to prepare for the flood risk. However, Jakarta is continually pressed by the population growth, averaging 1.27% in the past 20 years. The constant growth of Jakarta’s population continually develops slums in increasingly inconvenient locations, including the riverbanks, increasing vulnerability to floods. This situation necessitates a more advanced early warning system that could provide a longer forecasting lead time. Satellite remote sensing data propose a promising utility to extend the prediction lead time of extreme events. In the case of this study, Sadewa data is used to predict the water level of Katulampa Barrage using long short-term memory (LSTM) recurrent neural networks (RNN). The results show that the model could predict Katulampa Water Level accurately. The model presents a potential for implementation and additional lead time to increase flood mitigation preparedness.
Ciliwung River Basin is one of 13 watersheds located in the Special Capital Region of Jakarta (DKI Jakarta), one of the most vulnerable cities facing flood disaster. Modeling hydrographs in a flood computer model is a crucial process in understanding the hydraulic processes in floods; however, more often than have adequately not studied watersheds do not available observed data. Well-calibrated hydrographs serve as the basis for a basin-wide flood model. In this study, the Snyder, ITB, and SCS Unit Hydrograph are constructed in the study case of flooding in the Ciliwung River Basin, with West Java rainfall distribution method and Alternating Block Method as the adopted rainfall distributions, providing six combinations of approaches. Calibration and validation analysis show a combination of West Java rainfall distribution and SCS UH with a lag time of 5.5 gives the smallest deviation, with an accuracy of 83.3%, therefore it is recommended for use to estimate design future flood hydrograph.
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