The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed
The Majalaya area is one of the most valuable economic districts in the south of Greater Bandung, West Java, Indonesia, and experiences at least six floods per year. The floods are characterized by a sudden rise in the water level approximately one to two hours after the rain occurs. With the aim of...
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MDPI AG
2023-08-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/17/3026 |
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| author | Nabila Siti Burnama Faizal Immaddudin Wira Rohmat Mohammad Farid Arno Adi Kuntoro Hadi Kardhana Fauzan Ikhlas Wira Rohmat Winda Wijayasari |
| author_facet | Nabila Siti Burnama Faizal Immaddudin Wira Rohmat Mohammad Farid Arno Adi Kuntoro Hadi Kardhana Fauzan Ikhlas Wira Rohmat Winda Wijayasari |
| author_sort | Nabila Siti Burnama |
| collection | DOAJ |
| description | The Majalaya area is one of the most valuable economic districts in the south of Greater Bandung, West Java, Indonesia, and experiences at least six floods per year. The floods are characterized by a sudden rise in the water level approximately one to two hours after the rain occurs. With the aim of reducing flood risk, this study models a data-driven method for predicting the inundation height across the Majalaya Watershed. The flood inundation maps of selected events were modeled using the HEC-RAS 2D numerical model. Extracted data from the HEC-RAS model, GSMaP satellite rainfall data, elevation, and other spatial data were combined to build an artificial neural network (ANN) model. The trained model targets inundation height, while the spatiotemporal data serve as the explanatory variables. The results from the trained ANN model provided very good R<sup>2</sup> (0.9537), NSE (0.9292), and RMSE (0.3701) validation performances. The ANN model was tested with a new dataset to demonstrate the capability of predicting flood inundation height with unseen data. Such a data-driven approach is a promising tool to be developed to reduce flood risks in the Majalaya Watershed and other flood-prone locations. |
| first_indexed | 2024-03-10T23:10:24Z |
| format | Article |
| id | doaj.art-5d08b8ec9cf9468ea655097469e3b7c1 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T23:10:24Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-5d08b8ec9cf9468ea655097469e3b7c12023-11-19T09:01:15ZengMDPI AGWater2073-44412023-08-011517302610.3390/w15173026The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya WatershedNabila Siti Burnama0Faizal Immaddudin Wira Rohmat1Mohammad Farid2Arno Adi Kuntoro3Hadi Kardhana4Fauzan Ikhlas Wira Rohmat5Winda Wijayasari6Graduate School of Water Resources Engineering, Faculty of Civil Engineering, Bandung Institute of Technology, Jalan Ganesa No.10, Bandung 40132, IndonesiaWater Resources Development Center, Bandung Institute of Technology, Jalan Ganesa No.10, Bandung 40132, IndonesiaWater Resources Research Group, Faculty of Civil Engineering, Bandung Institute of Technology, Jalan Ganesa No.10, Bandung 40132, IndonesiaWater Resources Research Group, Faculty of Civil Engineering, Bandung Institute of Technology, Jalan Ganesa No.10, Bandung 40132, IndonesiaWater Resources Research Group, Faculty of Civil Engineering, Bandung Institute of Technology, Jalan Ganesa No.10, Bandung 40132, IndonesiaWater Resources Development Center, Bandung Institute of Technology, Jalan Ganesa No.10, Bandung 40132, IndonesiaDepartment of Computational Science, Faculty of Mathematics and Natural Science, Bandung Institute of Technology, Jalan Ganesa No.10, Bandung 40132, IndonesiaThe Majalaya area is one of the most valuable economic districts in the south of Greater Bandung, West Java, Indonesia, and experiences at least six floods per year. The floods are characterized by a sudden rise in the water level approximately one to two hours after the rain occurs. With the aim of reducing flood risk, this study models a data-driven method for predicting the inundation height across the Majalaya Watershed. The flood inundation maps of selected events were modeled using the HEC-RAS 2D numerical model. Extracted data from the HEC-RAS model, GSMaP satellite rainfall data, elevation, and other spatial data were combined to build an artificial neural network (ANN) model. The trained model targets inundation height, while the spatiotemporal data serve as the explanatory variables. The results from the trained ANN model provided very good R<sup>2</sup> (0.9537), NSE (0.9292), and RMSE (0.3701) validation performances. The ANN model was tested with a new dataset to demonstrate the capability of predicting flood inundation height with unseen data. Such a data-driven approach is a promising tool to be developed to reduce flood risks in the Majalaya Watershed and other flood-prone locations.https://www.mdpi.com/2073-4441/15/17/3026floodANNinundation height predictionsatellite rainfall |
| spellingShingle | Nabila Siti Burnama Faizal Immaddudin Wira Rohmat Mohammad Farid Arno Adi Kuntoro Hadi Kardhana Fauzan Ikhlas Wira Rohmat Winda Wijayasari The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed Water flood ANN inundation height prediction satellite rainfall |
| title | The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed |
| title_full | The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed |
| title_fullStr | The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed |
| title_full_unstemmed | The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed |
| title_short | The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed |
| title_sort | utilization of satellite data and machine learning for predicting the inundation height in the majalaya watershed |
| topic | flood ANN inundation height prediction satellite rainfall |
| url | https://www.mdpi.com/2073-4441/15/17/3026 |
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