Deep learning of backpropagation neural network algorithm for long-term predicting rainfall in the Kapuas Hulu, West Kalimantan province of Indonesia
Article Main
Abstract
Climate change and global warming significantly impact rainfall patterns in various regions. This can lead to more frequent and intense flooding and an increased risk of landslides. As a result, it causes unstable rain variability patterns in various regions, including Kapuas Hulu, West Kalimantan Province, Indonesia. Almost every year, the area experiences floods and landslides. The area, directly adjacent to the Indonesia-Malaysia region, can potentially disrupt community activities, including military operations guarding the border, which require a lot of manpower. This study aimed to minimize future disasters as it is vital to anticipate rainfall patterns based on previous data from databases. The Backpropagation Neural Network (BPNN) approach is one of the best at predicting long-term rainfall. Rainfall data from NASA was utilized from January 2003 through December 2020, totalling 216 data sets. The input or training data ranges from January 2003 to December 2010, whereas the training goal data is from January 2011 to December 2015. The validation data was also determined from January 2016 to December 2020. With a learning rate of 0.3 and an Epoch of 9,999, the best predictive architecture model was 8-6-9-6-5. The prediction accuracy was pretty excellent, with a mean square error (MSE) of 0.012157 and a mean absolute percentage error (MAPE) of 24.026. The highest rainfall was recorded in December 2019 at 606.672 mm/month. The prediction results are expected to serve as a reference for mitigating disasters such as floods and landslides to facilitate security operations in border areas.
Article Details
Article Details
Keywords
Backpropagation Neural Network (BPNN), Disaster, Flood, Prediction, Rainfall
References
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Al Mamun, A., Sohel, M., Mohammad, N., Sunny, M. S. H., Dipta, D. R., & Hossain, E. (2020). A comprehensive review of the load forecasting techniques using single and hybrid predictive models. IEEE Access, 8, 134911-134939. DOI:10.1109/ACCESS.2020.3010702.
Bapiyev, I. M., Aitchanov, B. H., Tereikovskyi, I. A., Tereikovska, L. A., & Korchenko, A. A. (2017). Deep neural networks in cyber attack detection systems. International Journal of Civil Engineering and Technology (IJCIET), 8(11), 1086-1092.
Chauhan, D., & Thakur, J. (2014). Data mining techniques for weather prediction: A review. International Journal on Recent and Innovation Trends in Computing and Communication, 2(8), 2184-2189. DOI: https://doi.org/10.17762/ijritcc.v2i8.3679.
Chen, G., Fu, K., Liang, Z., Sema, T., Li, C., Tontiwachwuthikul, P., & Idem, R. (2014). The genetic algorithm based back propagation neural network for MMP prediction in CO2- EOR process. Fuel, 126, 202-212. DOI : 10.1016/j.fuel.2014.02.034.
Fei, J., Zhao, N., Shi, Y., Feng, Y., & Wang, Z. (2016). Compressor performance prediction using a novel feed-forward neural network based on Gaussian kernel function. Advances in Mechanical Engineering, 8(1). https://doi.org/10.1177/1687814016628396.
Guha, S., Jana, R. K., & Sanyal, M. K. (2022). Artificial neural network approaches for disaster management: A literature review (2010–2021). International Journal of Disaster Risk Reduction, 103276. DOI:10.1016/j.ijdrr.2022.103276.
Hanak, E., & Lund, J. R. (2012). Adapting California’s water management to climate change.Climate Change, 111, 17-44. DOI:10.1007/s10584-011-0241-3
Karsoliya, S. (2012). Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. International Journal of Engineering Trends and Technology, 3(6), 714-717.
Kim, T., Ko, W., & Kim, J. (2019). Analysis and impact evaluation of missing data imputation in day-ahead PV generation forecasting. Applied Sciences, 9(1), 204. https://doi.org/10.3390/app9010204.
Kukreja, H., Bharath, N., Siddesh, C. S., & Kuldeep, S. (2016). An introduction to artificial neural network. International Journal of Advance Research and Innovative Ideas in Education, 1, 27-30.
Nayak, S. (2023). Exploring the Future Rainfall Characteristics over India from Large Ensemble Global Warming Experiments. Climate, 11(5), 94. https://doi.org/10.3390/cli11050094.
Oppenheimer, M., & Anttila-Hughes, J. K. (2016). The science of climate change. The Future of Children, 11-30. DOI : https://doi.org/10.1353/foc.2016.0001.
Pratama, M. B., Multazima, R., & Azkiarizqi, I. N. (2021). Hydro-Meteorological Aspects of the 2021 South Kalimantan Flood: Topography, Tides, and Precipitation. International Journal of Remote Sensing and Earth Sciences (IJReSES), 18(1), 73-90. DOI:10.31223/X5V89B
Putra, I. D. G. A., Nimiya, H., Sopaheluwakan, A., Kubota, T., Lee, H. S., Pradana, R. P., ... & Riama, N. F. (2022). Development of climate zones for passive cooling techniques in the hot and humid climate of Indonesia. Building and Environment, 226. DOI:10.1016/j.buildenv.2022.109698.
Qu, Z., Mao, W., Zhang, K., Zhang, W., & Li, Z. (2019). Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved backpropagation neural network. Renewable Energy, 133, 919-929. DOI: 10.1016/j.renene.2018.10.043.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back- propagating errors. Nature, 323(6088), 533-536. https://doi.org/10.10 38/323533a0.
Salman, R. S. (2016). Impact Of El Nino Phenomenon On Paddy Field At Seram Island (Case Study: El Nino 2015-2016). International Conference on Climate Change (pp. 248- 254). https://doi.org/10.15608/iccc.y2016.570.
Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems, 28. NIPS. https://doi.org/10/48550/arXiv.1506.04214.
Shihab, K. (2006). A backpropagation neural network for computer network security. Journal of Computer Science, 2(9), 710-715. DOI:10.3844/jcssp.2006.710.715.
Shirzadi, N., Nizami, A., Khazen, M., & Nik-Bakht, M. (2021). Medium-term regional electricity load forecasting through machine learning and deep learning. Designs, 5(2), 27. https://doi.org/10.3390/designs5020027.
Sohn, W., Kim, J. H., Li, M. H., Brown, R. D., & Jaber, F. H. (2020). How does increasing impervious surfaces affect urban flooding in response to climate variability?. Ecological Indicators, 118, 106774. DOI:10.1016/j.ecolind.2020.106774.
Tarigan, J., Diedan, R., & Suryana, Y. (2017). Plate recognition using backpropagation neural network and genetic algorithm. Procedia Computer Science, 116, 365-372. https://doi.org/10.1016/j.procs.2017.10.068.
Todini, E. (2007). Hydrological catchment modelling: past, present and future. Hydrology and Earth System Sciences, 11(1), 468-482. https://doi.org/10.5194/hess-11-468-2007
VanDeventer, W., Jamei, E., Thirunavukkarasu, G. S., Seyedmahmoudian, M., Soon, T. K., Horan, B., ... & Stojcevski, A. (2019). Short-term PV power forecasting using hybrid GASVM technique. Renewable Energy, 140, 367-379. DOI: 10.1016/j.renene.2019.02.087.
Werbos, P. (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences. PhD thesis, Committee on Applied Mathematics, Harvard University, Cambridge, MA.
Wica, M., Witkowsk, M., Szumiec, A., & Ziebura, T. (2019). Weather forecasting system with the use of neural network and backpropagation algorithm. In Proceedings of the International Conference on Data Engineering and Communication Technology; Springer: Singapore (Vol. 2468, pp. 37-41).
Zhang, E., Hou, L., Shen, C., Shi, Y., & Zhang, Y. (2015). Sound quality prediction of (BPNN) based on particle swarm vehicle interior noise and mathematical modeling using a back propagation neural network optimization (PSO). Measurement Science and Technology, 27(1), 015801. DOI 10.1088/0957-0233/27/1/015801.
Zhang, J., Zhang, R., Zhao, Y., Qiu, J., Bu, S., Zhu, Y., & Li, G. (2023). Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model. Energies, 16(10), 4237. https://doi.org/10.3390/en16104237.
Zhong, J., Liu, L., Sun, Q., & Wang, X. (2018). Prediction of photovoltaic power generation based on general regression and back propagation neural network. Energy Procedia, 152, 1224-1229. DOI:10.1016/j.egypro.2018.09.173.
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How to Cite
Deep learning of backpropagation neural network algorithm for long-term predicting rainfall in the Kapuas Hulu, West Kalimantan province of Indonesia. (2025). Journal of Applied and Natural Science, 17(1), 389-397. https://doi.org/10.31018/jans.v17i1.6183
