Water Level Time Series Forecasting Using TCN Study Case in Surabaya
Climate change is causing water levels to rise, leading to detrimental effects like tidal flooding in coastal areas. Surabaya, the capital of East Java Province in Indonesia, is particularly vulnerable due to its low-lying location. According to the Meteorological, Climatological, and Geophysical Ag...
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Department of Informatics, UIN Sunan Gunung Djati Bandung
2024-04-01
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| author | Deni Saepudin Egi Shidqi Rabbani Dio Navialdy Didit Adytia |
| author_facet | Deni Saepudin Egi Shidqi Rabbani Dio Navialdy Didit Adytia |
| author_sort | Deni Saepudin |
| collection | DOAJ |
| description | Climate change is causing water levels to rise, leading to detrimental effects like tidal flooding in coastal areas. Surabaya, the capital of East Java Province in Indonesia, is particularly vulnerable due to its low-lying location. According to the Meteorological, Climatological, and Geophysical Agency (BMKG), tidal flooding occurs annually in Surabaya as a result of rising water levels, highlighting the urgent need for water level forecasting models to mitigate these impacts. In this study, we employ the Temporal Convolutional Network (TCN) machine learning model for water level forecasting using data from a sea level station monitoring facility in Surabaya. We divided the training data into three scenarios: 3, 6, and 8 months to train TCN models for 14-day forecasts. The 8-month training scenario yielded the best results. Subsequently, we used the 8-month training data to forecast 1, 3, 7, and 14 days using TCN, Transformers, and the Recurrent Neural Network (RNN) models. TCN consistently outperformed other models, particularly excelling in 1-day forecasting with coefficient of determination () and RMSE values of 0.9950 and 0.0487, respectively. |
| format | Article |
| id | doaj-art-cfc23e0de66a4945b793e2385c86fcdb |
| institution | Kabale University |
| issn | 2528-1682 2527-9165 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Department of Informatics, UIN Sunan Gunung Djati Bandung |
| record_format | Article |
| series | JOIN: Jurnal Online Informatika |
| spelling | doaj-art-cfc23e0de66a4945b793e2385c86fcdb2025-08-20T03:25:55ZengDepartment of Informatics, UIN Sunan Gunung Djati BandungJOIN: Jurnal Online Informatika2528-16822527-91652024-04-0191616910.15575/join.v9i1.13121313Water Level Time Series Forecasting Using TCN Study Case in SurabayaDeni Saepudin0Egi Shidqi Rabbani1Dio Navialdy2Didit Adytia3School of Computing, Telkom University, BandungSchool of Computing, Telkom University, BandungSchool of Computing, Telkom University, BandungSchool of Computing, Telkom University, BandungClimate change is causing water levels to rise, leading to detrimental effects like tidal flooding in coastal areas. Surabaya, the capital of East Java Province in Indonesia, is particularly vulnerable due to its low-lying location. According to the Meteorological, Climatological, and Geophysical Agency (BMKG), tidal flooding occurs annually in Surabaya as a result of rising water levels, highlighting the urgent need for water level forecasting models to mitigate these impacts. In this study, we employ the Temporal Convolutional Network (TCN) machine learning model for water level forecasting using data from a sea level station monitoring facility in Surabaya. We divided the training data into three scenarios: 3, 6, and 8 months to train TCN models for 14-day forecasts. The 8-month training scenario yielded the best results. Subsequently, we used the 8-month training data to forecast 1, 3, 7, and 14 days using TCN, Transformers, and the Recurrent Neural Network (RNN) models. TCN consistently outperformed other models, particularly excelling in 1-day forecasting with coefficient of determination () and RMSE values of 0.9950 and 0.0487, respectively.https://join.if.uinsgd.ac.id/index.php/join/article/view/1312coastal areawater level forecastingmachine learningtcnwater level rise |
| spellingShingle | Deni Saepudin Egi Shidqi Rabbani Dio Navialdy Didit Adytia Water Level Time Series Forecasting Using TCN Study Case in Surabaya JOIN: Jurnal Online Informatika coastal area water level forecasting machine learning tcn water level rise |
| title | Water Level Time Series Forecasting Using TCN Study Case in Surabaya |
| title_full | Water Level Time Series Forecasting Using TCN Study Case in Surabaya |
| title_fullStr | Water Level Time Series Forecasting Using TCN Study Case in Surabaya |
| title_full_unstemmed | Water Level Time Series Forecasting Using TCN Study Case in Surabaya |
| title_short | Water Level Time Series Forecasting Using TCN Study Case in Surabaya |
| title_sort | water level time series forecasting using tcn study case in surabaya |
| topic | coastal area water level forecasting machine learning tcn water level rise |
| url | https://join.if.uinsgd.ac.id/index.php/join/article/view/1312 |
| work_keys_str_mv | AT denisaepudin waterleveltimeseriesforecastingusingtcnstudycaseinsurabaya AT egishidqirabbani waterleveltimeseriesforecastingusingtcnstudycaseinsurabaya AT dionavialdy waterleveltimeseriesforecastingusingtcnstudycaseinsurabaya AT diditadytia waterleveltimeseriesforecastingusingtcnstudycaseinsurabaya |