Water Level Time Series Forecasting Using TCN Study Case in Surabaya


  • Deni Saepudin School of Computing, Telkom University, Bandung, Indonesia
  • Egi Shidqi Rabbani School of Computing, Telkom University, Bandung, Indonesia
  • Dio Navialdy School of Computing, Telkom University, Bandung, Indonesia
  • Didit Adytia School of Computing, Telkom University, Bandung, Indonesia




Coastal Area, Water Level Forecasting, Machine Learning, TCN, Water Level Rise


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.


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