Monthly Rainfall Prediction Using the Backpropagation Neural Network (BPNN) Algorithm in Maros Regency
(1) Department of Physics, Universitas Negeri Makassar, Indonesia
(2) Department of Physics, Universitas Negeri Makassar, Indonesia
(3) Department of Physics, Universitas Negeri Makassar, Indonesia
(4) Department of Physics, Universitas Negeri Makassar, Indonesia
(5) Department of Physics, Universitas Negeri Makassar, Indonesia
(6) Badan Meteorologi, Klimatologi, dan Geofisika, Indonesia
Abstract
Purpose: This study aims to identify the right combination of network architecture, learning rate, and epoch in making predictions at each rainfall post in Maros Regency. In addition, this study also predicts the monthly rainfall profile in 2021-2025 in Maros Regency.
Methods: The method in this study is the backpropagation neural network algorithm to learn and predict the data. BPNN is one of the most commonly used non-linear methods in making predictions recently. The data used in this study is monthly rainfall data from 2000-2020 as training and testing data at four rainfall stations including BPP Batubassi, Staklim Maros, Stamet Hasanuddin, and BPP Tanralili.
Result: The results showed that the combination of network architecture, learning rate, and epoch obtained at each rainfall post was different. The highest level of prediction accuracy was obtained on 5 layers rather than 3 or 4 layers of network architecture with prediction accuracy at each rainfall post respectively 76.91%, 72.47%, 75.24%, and 76.53%. The predictions of rainfall from 2021-2025 are following the monsoon rain pattern with the highest rainfall in January 2025 of 964.1 mm, while the largest annual rainfall is obtained in 2023 with a total of 3359.6 mm.
Novelty: In this study, various combinations of network architecture parameters consisting of learning rate, epoch, and architecture at each rainfall post obtained different results. Particularly in the Maros Regency, the combination that is most suitable for use in predicting monthly rainfall at the Batubassi BPP post is learning rate 0.7, epoch 50000, and network architecture 11-6-10-7-5, at Staklim Maros post is learning rate 0.5, epoch 50000, and network architecture 11-5-9-10-5, at Stamet Hasanuddin post is learning rate 0.8, epoch 20000, and network architecture 11-5-8-6-5, and at BPP Tanralili post is learning rate 0.5, epoch 10000, and 11-5-9-9-5 network architecture.
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I. Hossain, H. M. Rasel, M. A. Imteaz, and F. Mekanik, “Long-term Seasonal Rainfall Forecasting Using Linear and Non-linear Modelling Approaches: a Case Study for Western Australia,” Meteorol. Atmos. Phys., vol. 132, no. 1, pp. 131–141, 2020, doi: https://doi.org/10.1007/s00703-019-00679-4.
Kalfin, Sukono, S. Supian, and M. Mamat, “Mitigation and Models for Determining Premiums for Natural Disaster Insurance Due to Excessive Rainfall,” J. Phys. Conf. Ser., vol. 1722, no. 1, pp. 1–8, 2021, doi: 10.1088/1742-6596/1722/1/012058.
L. Chen, P. Chen, and Z. Lin, “Artificial Intelligence in Education: A Review,” IEEE Access, vol. 8, pp. 75264–75278, 2020, doi: 10.1109/ACCESS.2020.2988510.
M. Doherty and B. Esmaeili, “Application of Artificial Intelligence in Electrical Safety,” in 2020 IEEE IAS Electr. Saf. Workshop (ESW), 2020, pp. 1–6, doi: 10.1109/ESW42757.2020.9188324.
P. Pampouktsi, S. Avdimiotis, M. Maragoudakis, M. Avlonitis, P. Hoogar, and G. Ruhago, “Techniques of Applied Machine Learning Being Utilized for the Purpose of Selecting and Placing Human Resources within the Public Sector,” J. Inf. Syst. Explor. Res., vol. 01, no. 01, pp. 1–16, 2023.
H. Salehi and R. Burgueño, “Emerging Artificial Intelligence Methods in Structural Engineering,” Eng. Struct., vol. 171, pp. 170–189, 2018, doi: https://doi.org/10.1016/j.engstruct.2018.05.084.
A. Mosavi, P. Ozturk, and K. W. Chau, “Flood Prediction Using Machine Learning Models: Literature Review,” Water (Switzerland), vol. 10, no. 11, pp. 1–40, 2018, doi: https://doi.org/10.3390/w10111536.
E. M. C. Wattimena and I. S. Sitanggang, “CO and PM 10 Prediction Model based on Air Quality Index Considering Meteorological Factors in DKI Jakarta Using LSTM,” Sci. J. Inform., vol. 9, no. 2, pp. 123–132, 2022, doi: 10.15294/sji.v9i2.33791.
D. R. Damayanti, S. Wicaksono, M. F. Al Hakim, J. Jumanto, S. Subhan, and Y. N. Ifriza, “Rainfall Prediction in Blora Regency Using Mamdani’s Fuzzy Inference System,” J. Soft Comput. Explor., vol. 3, no. 1, pp. 62–69, 2022, doi: 10.52465/joscex.v3i1.69.
A. Agustyawan, T. G. Laksana, and U. Athiyah, “Combination of Backpropagation Neural Network and Particle Swarm Optimization for Water Production Prediction in Municipal Waterworks,” Sci. J. Inform., vol. 9, no. 1, pp. 84–94, 2022, doi: 10.15294/sji.v9i1.29849.
R. Rosita, D. Ananda Agustina Pertiwi, and O. Gina Khoirunnisa, “Prediction of Hospital Intesive Patients Using Neural Network Algorithm,” J. Soft Comput. Explor., vol. 3, no. 1, pp. 8–11, 2022, doi: 10.52465/joscex.v3i1.61.
T. Canchala, W. Alfonso-Morales, Y. Carvajal-Escobar, W. L. Cerón, and E. Caicedo-Bravo, “Monthly Rainfall Anomalies Forecasting for Southwestern Colombia Using Artificial Neural Networks Approaches,” Water (Switzerland), vol. 12, no. 9, pp. 1–23, 2020, doi: 10.3390/w12092628.
G. Zhang, B. Eddy Patuwo, and M. Y. Hu, “Forecasting with Artificial Neural Networks:: The State of the Art,” Int. J. Forecast., vol. 14, no. 1, pp. 35–62, 1998, doi: https://doi.org/10.1016/S0169-2070(97)00044-7.
Y. Prayoga, D. Hartama, J. Jalaluddin, S. Sumarno, and Z. M. Nasution, “Increasing Prediction Accuracy with the Backpropagation Algorithm (Case Study: Pematangsiantar City Rainfall),” Int. J. Inf. Syst. Technol. (IJISTECH), vol. 3, no. 1, p. 11, 2019, doi: 10.30645/ijistech.v3i1.27.
Y. A. Lesnussa, C. G. Mustamu, F. Kondo Lembang, and M. W. Talakua, “Application of Backpropagation Neural Networks in Predicting Rainfall Data in Ambon City,” Int. J. Artif. Intell. Res., vol. 2, no. 2, 2018, doi: 10.29099/ijair.v2i2.59.
P. D. H. Ardana, I. G. M. Sudika, N. K. Astariani, and G. Sumarda, “Application of Feed Forward Backpropagation Neural Network in Monthly Rainfall Prediction,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 8, no. 1.5, pp. 192–198, 2019, doi: https://doi.org/10.30534/ijatcse/2019/3681.52019.
J. H. Abdulkareem, B. Pradhan, W. N. A. Sulaiman, and N. R. Jamil, “Review of Studies on Hydrological Modelling in Malaysia,” Model. Earth Syst. Environ., vol. 4, no. 4, pp. 1577–1605, 2018, doi: 10.1007/s40808-018-0509-y.
A. S. Rajawat and S. Jain, “Fusion Deep Learning Based on Back Propagation Neural Network for Personalization,” in 2nd Int. Conf. Data Eng. Appl. (IDEA), 2020, pp. 1–7, doi: 10.1109/IDEA49133.2020.9170693.
R. Barkey, M. Nursaputra, M. F. Mappiase, M. Achmad, M. Solle, and M. Dassir, “Climate Change Impacts Related Flood Hazard to Communities Around Bantimurung Bulusaraung National Park, Indonesia,” IOP Conf. Ser. Earth Environ. Sci., vol. 235, no. 1, p. 12022, 2019, doi: 10.1088/1755-1315/235/1/012022.
M. Vafakhah, “Comparison of Cokriging and Adaptive Neuro-fuzzy Inference System Models for Suspended Sediment Load Forecasting,” Arab. J. Geosci., vol. 6, no. 8, pp. 3003–3018, 2013, doi: 10.1007/s12517-012-0550-5.
S. Chakraverty and S. K. Jeswal, Applied Artificial Neural Network Methods for Engineers and Scientists. World Scientific Pub Co Inc, 2021.
S. Shanmuganathan and S. Samarasinghe, Artificial Neural Network Modelling. Springer Cham, 2016.
S. Prayudani, A. Hizriadi, Y. Y. Lase, Y. Fatmi, and Al-Khowarizmi, “Analysis Accuracy Of Forecasting Measurement Technique On Random K-Nearest Neighbor (RKNN) Using MAPE And MSE,” J. Phys. Conf. Ser., vol. 1361, no. 1, p. 12089, 2019, doi: 10.1088/1742-6596/1361/1/012089.
A. P. Widodo, E. A. Sarwoko, and Z. Firdaus, “Akurasi Model Prediksi Metode Backpropagation Menggunakan Kombinasi Hidden Neuron Dengan Alpha,” Matematika, vol. 20, no. 2, pp. 79–84, 2017.
S. Karsoliya, “Approximating Number of Hidden Layer Neurons in Multiple Hidden Layer BPNN Architecture,” Int. J. Eng. Trends Technol., vol. 3, no. 6, pp. 714–717, 2012.
S. Anam, “Rainfall Prediction Using Backpropagation Algorithm Optimized by Broyden-Fletcher-Goldfarb-Shanno Algorithm,” IOP Conf. Ser. Mater. Sci. Eng., vol. 567, no. 1, p. 12008, 2019, doi: 10.1088/1757-899X/567/1/012008.
C. Oktaviani and A. Afdal, “Prediksi Curah Hujan Bulanan Menggunakan Jaringan Syaraf Tiruan dengan Beberapa Fungsi Pelatihan Backpropagation (Studi Kasus: Stasiun Meteorologi Tabing Padang, Tahun 2001-2012),” J. Fis. Unand, vol. 2, no. 4, pp. 228–237, 2014, doi: https://doi.org/10.25077/jfu.2.4.%25p.2013.
H. Thirafi, “La Nina Bertahan Hingga Mei 2021, 58% Wilayah Zona Musim Terlambat Masuk Musim Kemarau,” Badan, Meteorologi, Klimatologi dan Geofisika (BMKG), March 25, 2021. [Online]. Available: https://www.bmkg.go.id/berita/?p=la-nina-bertahan-hingga-mei-2021-58-wilayah-zona-musim-terlambat-masuk-musim-kemarau〈=ID&tag=press-release. [Accessed Nov 06, 2021].
N. Badwi, I. I. Baharuddin, and I. Abbas, “Flood Hazard Level Mapping in Maros River Basin,” La Geogr., vol. 18, no. 3, pp. 309–322, 2020, doi: https://doi.org/10.35580/lageografia.v18i3.13930.
S. A. Lias, Irmayani, and S. Laban, “The Potential of Water Availability in Maros Watershed Using Thornthwaite-Mather Water Balance Method,” in IOP Conf. Ser.: Earth Environ. Sci., 2020, vol. 486, no. 1, pp. 1–6, doi: 10.1088/1755-1315/486/1/012135.
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