Loading...
Please wait, while we are loading the content...
Improving Jakarta's Katulampa Barrage Extreme Water Level Prediction Using Satellite-Based Long Short-Term Memory (LSTM) Neural Networks
| Content Provider | MDPI |
|---|---|
| Author | Kardhana, Hadi Valerian, Jonathan Raditya Rohmat, Faizal Immaddudin Wira Kusuma, Muhammad Syahril Badri |
| Copyright Year | 2022 |
| Description | 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. |
| Starting Page | 1469 |
| e-ISSN | 20734441 |
| DOI | 10.3390/w14091469 |
| Journal | Water |
| Issue Number | 9 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-04 |
| Access Restriction | Open |
| Subject Keyword | Water Remote Sensing Flood Early Warning Machine Learning Recurrent Neural Networks Lstm Jakarta |
| Content Type | Text |
| Resource Type | Article |
