Tidal flood prediction in Indonesian coastal areas using long short-term memory for enhanced early warning systems
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Abstract
Coastal flooding, locally known as banjir rob, persists as a recurring hazard in Indonesia’s low-lying coastal zones, driven by tidal variation, river discharge, and meteorological dynamics. This study applies a Long Short-Term Memory (LSTM) neural network for short-term flood prediction using a multivariate dataset covering 2020–2024. The dataset integrates daily records of water levels from six monitoring stations (Katulampa, Pos Depok, Manggarai, Istiqlal, Jembatan Merah, Flusing Ancol), sea-level observations from Marina Ancol, and meteorological parameters including wind speed, wind direction, rainfall, atmospheric pressure, and sea surface temperature. Flood status was encoded as a binary target (0 = non-flood, 1 = flood) with balanced distribution, enabling robust model generalization. Preprocessing involved data cleaning, normalization, and sliding-window sequencing to capture temporal dependencies. The LSTM architecture combined stacked recurrent layers, dropout regularization, and a dense output layer, trained in TensorFlow with tuned hyperparameters. Evaluation indicated strong predictive skill, with Mean Absolute Error (MAE) below 3 cm, Mean Absolute Percentage Error (MAPE) under 2%, and classification accuracy above 90%. Comparative analysis demonstrated consistent outperformance of LSTM over Artificial Neural Networks (ANN) and linear regression, both of which produced higher errors and weaker representation of temporal patterns. The findings confirm LSTM’s capacity to support operational early warning systems, strengthen community preparedness, and mitigate socio-economic impacts in vulnerable coastal regions.
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Perdana, A. M. P. . (2025). Tidal flood prediction in Indonesian coastal areas using long short-term memory for enhanced early warning systems. Journal of Intelligent Decision Support System (IDSS), 8(3), 134-143. https://doi.org/10.35335/idss.v8i3.304
References
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Harintaka, H., Suhadha, A. G., Syetiawan, A., Ardha, M., & Rarasati, A. (2024). Current land subsidence in Jakarta: a multi-track SBAS InSAR analysis during 2017–2022 using C-band SAR data. Geocarto International, 39(1). https://doi.org/10.1080/10106049.2024.2364726
Hilal, Y., Nainggolan, G., Syahputri, S., & Kartiasih, F. (2024). COMPARISON OF ARIMA AND LSTM METHODS IN PREDICTING JAKARTA SEA LEVEL. Jurnal Ilmu Dan Teknologi Kelautan Tropis, 16, 163–178. https://doi.org/10.29244/jitkt.v16i2.52818
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Puspita Wulandari, E. S., Nurpambudi, R., & Aziz, RZ. A. (2023). Prediction model with artificial neural network for tidal flood events in the coastal area of bandar lampung City. Jurnal Infotel, 15(2), 1–15. https://doi.org/10.20895/infotel.v15i2.882
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S.K.B, S., Mathivanan, S. K., Rajadurai, H., Cho, J., & Easwaramoorthy, S. V. (2024). A multi-modal geospatial–temporal LSTM based deep learning framework for predictive modeling of urban mobility patterns. Scientific Reports, 14(1), 1–19. https://doi.org/10.1038/s41598-024-74237-3
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Waqas, M., & Humphries, U. (2024). A Critical Review of RNN and LSTM Variants in Hydrological Time Series Predictions. MethodsX, 13. https://doi.org/10.1016/j.mex.2024.102946
Xu, Y., Stephens, K. K., Carlson, N. H., Lieberknecht, K. E., & Leite, F. (2024). Moving toward community preparedness efficacy: Uncovering barriers in communities disproportionately impacted by flooding. Journal of Contingencies and Crisis Management, 32(1). https://doi.org/10.1111/1468-5973.12524
Yu, Y., Wan, D., Zhao, Q., & Liu, H. (2020). Detecting Pattern Anomalies in Hydrological Time Series with Weighted Probabilistic Suffix Trees. Water, 12(1464). https://doi.org/doi:10.3390/w12051464
Zhang, W., Yin, C., Liu, H., Zhou, X., & Xiong, H. (2024). Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach. Proceedings of Machine Learning Research, 235, 60179–60196.
Zhou, J., Chen, L., Hu, T., Lu, H., Shi, Y., & Chen, L. (2025). The comparative study of machine learning agent models in flood forecasting for tidal river reaches. Scientific Reports, 15(1), 1–17. https://doi.org/10.1038/s41598-025-04633-w
Accarino, G., Chiarelli, M., Fiore, S., Federico, I., Causio, S., Coppini, G., & Aloisio, G. (2021). A multi-model architecture based on Long Short-Term Memory neural networks for multi-step sea level forecasting. Future Generation Computer Systems, 124, 1–9. https://doi.org/https://doi.org/10.1016/j.future.2021.05.008
Almaliki, A. H., & Khattak, A. (2025). Short- and long-term tidal level forecasting: A novel hybrid TCN + LSTM framework. Journal of Sea Research, 204(March), 102577. https://doi.org/10.1016/j.seares.2025.102577
Ambadar, P. R., Novitasari, D. C. R., Farida, Y., Hafiyusholeh, M., & Setiawan, F. (2025). Implementation of LSTM Method on Tidal Prediction in Semarang Region. Journal of Applied Informatics and Computing, 9(2), 398–304. https://doi.org/10.30871/jaic.v9i2.8932
Arun, P. L., & Kumar, R. M. S. (2021). Non-linear Sorenson–Dice Exemplar Image Inpainting Based Bayes Probability for Occlusion Removal in Remote Traffic Control. Multimedia Tools and Applications, 80(8), 11523–11538. https://doi.org/10.1007/s11042-020-10060-y
Asrofi, A., Giyarsih, S. R., & Hadmoko, D. S. (2024). The Impact Of Tidal Floods On Poor Households In The Sayung Coast, Demak Regency, Indonesia. Indonesian Journal of Geography, 56(3), 456–465. https://doi.org/10.22146/ijg.94063
Awan, S. E., Bennamoun, M., Sohel, F., Sanfilippo, F., & Dwivedi, G. (2022). A reinforcement learning-based approach for imputing missing data. Neural Computing and Applications, 34(12), 9701–9716. https://doi.org/10.1007/s00521-022-06958-3
Bennett, W. G., Karunarathna, H., Xuan, Y., Kusuma, M. S. B., Farid, M., Kuntoro, A. A., Rahayu, H. P., Kombaitan, B., Septiadi, D., Kesuma, T. N. A., Haigh, R., & Amaratunga, D. (2023). Modelling compound flooding: a case study from Jakarta, Indonesia. Natural Hazards, 118(1), 277–305. https://doi.org/10.1007/s11069-023-06001-1
Bott, L. M., Schöne, T., Illigner, J., Haghshenas Haghighi, M., Gisevius, K., & Braun, B. (2021). Land subsidence in Jakarta and Semarang Bay – The relationship between physical processes, risk perception, and household adaptation. Ocean and Coastal Management, 211. https://doi.org/10.1016/j.ocecoaman.2021.105775
Boutin, J., Yueh, S., Bindlish, R., Chan, S., Entekhabi, D., Kerr, Y., Kolodziejczyk, N., Lee, T., Reul, N., & Zribi, M. (2023). Soil Moisture and Sea Surface Salinity Derived from Satellite-Borne Sensors. In Surveys in Geophysics (Vol. 44, Issue 5). Springer Netherlands. https://doi.org/10.1007/s10712-023-09798-5
Busker, T. S. (2024). Anticipating Weather and Climate Extremes . In The Value of Early Action to Reduce Disaster Impacts . https://doi.org/10.5463/thesis.837
Cubillos, M., Wøhlk, S., & Wulff, J. N. (2022). A bi-objective k-nearest-neighbors-based imputation method for multilevel data. Expert Systems with Applications, 204(February). https://doi.org/10.1016/j.eswa.2022.117298
Dash, N., Chakravarty, S., Rath, A. K., Giri, N. C., AboRas, K. M., & Gowtham, N. (2025). An optimized LSTM-based deep learning model for anomaly network intrusion detection. Scientific Reports, 15(1), 1–21. https://doi.org/10.1038/s41598-025-85248-z
De Leonardis, G., Rosati, S., Balestra, G., Agostini, V., Panero, E., Gastaldi, L., & Knaflitz, M. (2018). Human Activity Recognition by Wearable Sensors : Comparison of different classifiers for real-time applications. MeMeA 2018 - 2018 IEEE International Symposium on Medical Measurements and Applications, Proceedings, 3528725544, 1–6. https://doi.org/10.1109/MeMeA.2018.8438750
Flores-Palacios, X., Ahmed, B., & Barbera, C. (2023). Micro-narratives on people’s perception of climate change and its impact on their livelihood and migration: Voices from the indigenous aymara people in the Bolivian Andes. Rebuilding Communities After Displacement: Sustainable and Resilience Approaches, 29–57. https://doi.org/10.1007/978-3-031-21414-1_2
Hanif, M., Putra, B. G., Hidayat, R. A., Ramadhan, R., Ahyuni, A., Afriyadi, A., Wan Moh Jaafar, W. S., Hermon, D., & Mokhtar, E. S. (2021). Impact of Coastal Flood on Building, Infrastructure, and Community Adaptation in Bukit Bestari Tanjungpinang. Jurnal Geografi Gea, 21(2), 102–111. https://doi.org/10.17509/gea.v21i2.38911
Harintaka, H., Suhadha, A. G., Syetiawan, A., Ardha, M., & Rarasati, A. (2024). Current land subsidence in Jakarta: a multi-track SBAS InSAR analysis during 2017–2022 using C-band SAR data. Geocarto International, 39(1). https://doi.org/10.1080/10106049.2024.2364726
Hilal, Y., Nainggolan, G., Syahputri, S., & Kartiasih, F. (2024). COMPARISON OF ARIMA AND LSTM METHODS IN PREDICTING JAKARTA SEA LEVEL. Jurnal Ilmu Dan Teknologi Kelautan Tropis, 16, 163–178. https://doi.org/10.29244/jitkt.v16i2.52818
Iman, Rizky, A., Ardiyansyah, & Pratama, W. N. (2024). Penentuan Nilai Ambang Batas Dalam Prakiraan Potensi Banjir Pesisir di Utara Jakarta. Juli, 5(4), 35–43.
Jafarzadegan, K., Moradkhani, H., Pappenberger, F., Moftakhari, H., Bates, P., Abbaszadeh, P., Marsooli, R., Ferreira, C., Cloke, H. L., Ogden, F., & Duan, Q. (2023). Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling. Reviews of Geophysics, 61(2). https://doi.org/10.1029/2022rg000788
Jesus, G., Mardani, Z., Alves, E., & Oliveira, A. (2025). Deep Learning-Based River Flow Forecasting with MLPs: Comparative Exploratory Analysis Applied to the Tejo and the Mondego Rivers. Sensors, 25(7), 1–27. https://doi.org/10.3390/s25072154
Julio-alejandro, J. T. D. C. (2025). A comprehensive survey of loss functions and metrics in deep learning. Artificial Intelligence Review, 58, 1–172. https://doi.org/https://doi.org/10.1007/s10462-025-11198-7 A
Khan, S. Z., Muzammil, N., Ghafoor, S., Khan, H., Zaidi, S. M. H., Aljohani, A. J., & Aziz, I. (2024). Quantum long short-term memory (QLSTM) vs. classical LSTM in time series forecasting: a comparative study in solar power forecasting. Frontiers in Physics, 12(October), 1–15. https://doi.org/10.3389/fphy.2024.1439180
Li, A., & Kang, L. (2009). KNN-based modeling and its application in aftershock prediction. International Asia Symposium on Intelligent Interaction and Affective Computing, ASIA 2009, 83–86. https://doi.org/10.1109/ASIA.2009.21
Puspita Wulandari, E. S., Nurpambudi, R., & Aziz, RZ. A. (2023). Prediction model with artificial neural network for tidal flood events in the coastal area of bandar lampung City. Jurnal Infotel, 15(2), 1–15. https://doi.org/10.20895/infotel.v15i2.882
Rahadiati, A., Syetiawan, A., Ambarwulan, W., Ardha, M., & Hafsaridewi, R. (2025). Assessing the Resilience of Coastal Suburbs to Floods Based on Socio-Environmental Factors: A Case Study on Tangerang Coast, Java, Indonesia. Polish Journal of Environmental Studies, 34(4), 3741–3753. https://doi.org/10.15244/pjoes/188904
Rosida, E., . S., & Marfai, M. A. (2020). Environmental Quality Analysis Due To Tidal Flood in Pasirkratonkramat Sub-District Pekalongan, Indonesia. Environment & Ecosystem Science, 4(2), 55–61. https://doi.org/10.26480/ees.02.2020.55.61
Sakagianni, A., Koufopoulou, C., Feretzakis, G., Kalles, D., Verykios, V. S., Myrianthefs, P., & Fildisis, G. (2023). Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review. Antibiotics, 12(3), 1–18. https://doi.org/10.3390/antibiotics12030452
Sarwono, J. (2022). Quantitative, Qualitative and Mixed Method Research Methodology.
Selvarajan, G. P. (2024). Harnessing AI-Driven Data Mining for Predictive Insights : A Framework for Enhancing Decision- Making in Dynamic Data Environments. February 2021.
S.K.B, S., Mathivanan, S. K., Rajadurai, H., Cho, J., & Easwaramoorthy, S. V. (2024). A multi-modal geospatial–temporal LSTM based deep learning framework for predictive modeling of urban mobility patterns. Scientific Reports, 14(1), 1–19. https://doi.org/10.1038/s41598-024-74237-3
Upadhyaya, A., Debnath, P., Rani, P., P., S., Manjunath, T., & Bhattacharya, C. D. S. (2025). AI-Based Disaster Prediction and Early Warning Systems (pp. 55–74). https://doi.org/10.4018/979-8-3693-9770-1.ch003
Waqas, M., & Humphries, U. (2024). A Critical Review of RNN and LSTM Variants in Hydrological Time Series Predictions. MethodsX, 13. https://doi.org/10.1016/j.mex.2024.102946
Xu, Y., Stephens, K. K., Carlson, N. H., Lieberknecht, K. E., & Leite, F. (2024). Moving toward community preparedness efficacy: Uncovering barriers in communities disproportionately impacted by flooding. Journal of Contingencies and Crisis Management, 32(1). https://doi.org/10.1111/1468-5973.12524
Yu, Y., Wan, D., Zhao, Q., & Liu, H. (2020). Detecting Pattern Anomalies in Hydrological Time Series with Weighted Probabilistic Suffix Trees. Water, 12(1464). https://doi.org/doi:10.3390/w12051464
Zhang, W., Yin, C., Liu, H., Zhou, X., & Xiong, H. (2024). Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach. Proceedings of Machine Learning Research, 235, 60179–60196.
Zhou, J., Chen, L., Hu, T., Lu, H., Shi, Y., & Chen, L. (2025). The comparative study of machine learning agent models in flood forecasting for tidal river reaches. Scientific Reports, 15(1), 1–17. https://doi.org/10.1038/s41598-025-04633-w
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