Penerapan Deep Learning untuk Prediksi Tinggi Muka Air Sungai dengan Mempertimbangkan Faktor Operasi Bendungan - Tamamadin, Mamad; Subrata, Oky; Akrom, Isnan Fauzan (Himpunan Ahli Teknik Hidraulik Indonesia (HATHI), 2025)
Indonesia is a flood-prone region, making early detection through accurate river water level prediction essential by utilizing efficient modeling methods. This study aims to develop a high-accuracy water level (TMA) prediction model at several river flow monitoring stations by employing spatial rainfall map datasets and dam discharge information as input variables in a deep learning framework that combines CNN and LSTM architectures. The model was tested under two scenarios, with and without dam operation, and its predictive performance was evaluated at three monitoring sites (Katulampa, Kampung Kalapa, and MT. Haryono). The initial evaluation using only spatial rainfall input for the MT. Haryono station showed a correlation coefficient of 0.65, MAE of 0.412 m, and NSE of 0.58. After incorporating multiple rainfall images and integrating discharge data based on dam operation scenarios, a significant improvement in prediction accuracy was observed across all stations, with the correlation increasing to 0.88, MAE decreasing to 0.137 m, and NSE rising to 0.85. These findings confirm that the inclusion of additional hydrological information, particularly dam operation data, can substantially enhance the reliability of river water level prediction models.