A Comparative Study of Box–Jenkins and Machine Learning Model for Water Level Prediction

Abstract

The Pasar Ikan Water Gate, located in North Jakarta, is a strategic area that is prone to coastal flooding (rob). The water level in this area is influenced by a combination of tides, high rainfall, and poor drainage systems, making it a frequent early indicator of potential flooding. This study uses daily water level measurement data from the Fish Market Water Gate from September 1 to October 24, 2024. The data patterns show that the data is quite fluctuating and exhibits seasonal patterns. Therefore, this study aims to compare the performance of the classical method, seasonal autoregressive integrated moving average (SARIMA), with the machine learning-based method, long short-term memory (LSTM). The analysis results show that the best SARIMA model is the ARIMA(1,0,3)×(1,2,1)_24 model with a mean absolute percentage error (MAPE) of 10.88% and a root mean square error (RMSE) of 25.45. However, this model has weaknesses due to unmet model assumptions, namely the assumptions of normality and homogeneity of residual variance. The LSTM modeling results indicate that the model with a combination of 50 epochs and a batch size of 1 produces the best LSTM model with MAPE and RMSE of 2.58% and 5.60, respectively. The LSTM method's prediction results are better than the SARIMA method's because they still follow the actual data pattern and show an upward trend, whereas the SARIMA method's forecasting results fail to follow this pattern and do not meet the assumptions.