Prediction of Rainfall and Water Discharge in The Jagir River Surabaya with Long-Short-Term Memory (LSTM)
Abstract
Flood disasters can occur at any time when the factors for the amount of river water discharge and rainfall intensity tend to be high, so preparations and ways of handling are needed to anticipate flood disasters quickly, precisely, and accurately for the Surabaya Public Works Service. One of the steps to predict and analyze the status of the flood disaster alert level is by calculating predictions based on rainfall and the amount of river water discharge. This study uses the Long-Short Term Memory (LSTM) algorithm to predict rainfall and river water discharge on the Jagir River in Surabaya. The LSTM method is a model commonly used for predictions based on time series data. The data obtained are rainfall data and water discharge on the Jagir River, Surabaya, which will be used as training and testing data to make predictions. The results of implementing the LSTM method using data training of 70% and data testing of 30% on rainfall data using the best epoch, namely at epoch ten by producing tests on data testing can have a Mean Absolute Error (MAE) performance of 4.5 and Root Mean Square Error (RMSE) of 9.7. Whereas the water discharge variable uses the best epoch, namely at epoch 75, by producing data testing data which can have a Mean Absolute Error (MAE) performance of 11.49 and a Root Mean Square Error (RMSE) of 9.63.
Downloads
References
Asdak, C. (2023). Hidrologi dan Pengelolaan Daerah Aliran Sungai. Yogjakarta: UGM PRESS.
Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. A. (2018). Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies, 11(7). https://doi.org/10.3390/en11071636
Devi, N. M. M. C., Bayupati, I. P. A., & Wirdiani, N. K. A. (2022). Prediksi Curah Hujan Dasarian dengan Metode Vanilla RNN dan LSTM untuk Menentukan Awal Musim Hujan dan Kemarau. JEPIN, 8(3), 405–411. Retrieved from https://jurnal.untan.ac.id/index.php/jepin/article/view/56606
Elizabeth Michael, N., Mishra, M., Hasan, S., & Al-Durra, A. (2022). Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique. Energies, 15(6). https://doi.org/10.3390/en15062150
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Kardhana, H., Valerian, J. R., Rohmat, F. I. W., & Kusuma, M. S. B. (2022). Improving Jakarta’s Katulampa Barrage Extreme Water Level Prediction Using Satellite-Based Long Short-Term Memory (LSTM) Neural Networks. Water, 14(9), 1–17. https://doi.org/10.3390/w14091469
Kouadri, S., Pande, C. B., Panneerselvam, B., Moharir, K. N., & Elbeltagi., A. (2022). Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models. Environmental Science and Pollution Research, 29, 21067–21091. https://doi.org/10.1007/s11356-021-17084-3
Larose, D. T. (2005). Discovering Knowledge in Data: An Introduction to Data Mining. Discovering Knowledge in Data: An Introduction to Data Mining, 2nd ed., pp. 1–222. New Jersey: John Willey & Sons Inc. https://doi.org/10.1002/0471687545
Navlan, A., Fandango, A., & Idris, I. (2021). Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python. Birmingham, United Kingdom: Packt Publishing Ltd.
Noymanee, J., & Theeramunkong, T. (2019). Flood Forecasting with Machine Learning Technique on Hydrological Modeling. Procedia Computer Science, 156, 377–386. https://doi.org/10.1016/j.procs.2019.08.214
Rizki, M., Basuki, S., & Azhar, Y. (2020). Implementasi Deep Learning Menggunakan Arsitektur Long Short Term Memory(LSTM) Untuk Prediksi Curah Hujan Kota Malang. Jurnal Repositor, 2(3), 331–338. https://doi.org/10.22219/repositor.v2i3.470
Sampurno, J., Vallaeys, V., Ardianto, R., & Hanert, E. (2022). Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta. Nonlinear Processes in Geophysics, 29(3), 301–315. https://doi.org/10.5194/npg-29-301-2022
Shetty, S. A., Padmashree, T., Sagar, B. M., & Cauvery, N. K. (2021). Performance Analysis on Machine Learning Algorithms with Deep Learning Model for Crop Yield Prediction. Data Intelligence and Cognitive Informatics, 739–750. Springer, Singapore. https://doi.org/10.1007/978-981-15-8530-2_58
Sudriani, Y., Ridwansyah, I., & A Rustini, H. (2019). Long short term memory (LSTM) recurrent neural network (RNN) for discharge level prediction and forecast in Cimandiri river, Indonesia. IOP Conference Series: Earth and Environmental Science, 299(1). https://doi.org/10.1088/1755-1315/299/1/012037
Supatmi, S., Hou, R., & Sumitra, I. D. (2019). Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia. Computational Intelligence and Neuroscience, 2019, 1–12. https://doi.org/10.1155/2019/6203510
Szandała, T. (2020). Review and Comparison of Commonly Used Activation Functions for Deep Neural Networks. In Bio-inspired Neurocomputing (pp. 203–224). Springer, Singapore. https://doi.org/10.1007/978-981-15-5495-7_11
Wang, W., & Lu, Y. (2018). Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. IOP Conference Series: Materials Science and Engineering, 324(1). https://doi.org/10.1088/1757-899X/324/1/012049
Abstract viewed = 44 times
PDF downloaded = 26 times
Copyright (c) 2023 Retzi Yosia Lewu, Slamet Slamet, Sri Wulandari, Widdi Djatmiko, Kusrini Kusrini, Mulia Sulistiyono
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
An author who publishes in the Jurnal Riset Informatika agrees to the following terms:
- The author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal
- The author is permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.
2.png){kind=logo}
