Backpropagation and Radial Basis Function Methods for Predicting Rainfall in Sukabumi City Using Artificial Neural Networks: A Comparative Analysis
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Abstract
The weather has a substantial impact on the ability to live organisms to carry out everyday activities, particularly outside activities. Weather data is helpful in various fields, including marine, aviation, and agriculture. The maritime domain is beneficial for establishing the optimal navigation time for a fisherman, the aviation domain helps reduce climate-related mishaps, and the agriculture sector uses weather information to develop harvest season models for agricultural products. Indonesia is a tropical nation with heavy precipitation. Utilized for various objectives, rainfall forecasting models seek the utmost precision, particularly in specialized areas such as flood control. This study is based on two techniques: the Radial Basis Function Neural Network (RBFNN) and Backpropagation Neural Network (BPNN) techniques using multiple training functions. The RBFNN approach yields less accurate results for predicting precipitation, but the multi-practice BPNN method yields more accurate results.
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Accepted 2022-04-21
Published 2022-05-31
References
Fitri, N. A., & Taufiq, I. (2020). Perbandingan JST Metode Backpropagation dan Metode Radial Basis dalam Memprediksi Curah Hujan Harian Bandara Internasional Minangkabau. Jurnal Fisika Unand (JFU) Vol. 9, No. 2, April 2020, hal. 217–223.
Resi, V.R. (n.d.). Analisis Perbandingan Metode Backpropagation Dan Radial Basis Function Untuk Mem Prediksi Curah Hujan Dengan Jaringan Syaraf Tiruan. http://eprints.dinus.ac.id/13485/1/jurnal_14146.pdf
E. S. Puspita and L. Yulianti, "Perancangan Sistem Peramalan Cuaca Berbasis Logika Fuzzy," J. Media Infotama, vol. 12, no. 1, 2016, doi: 10.37676/jmi.v12i1.267.
G. D. Winarno, S. P. Harianto, and T. Santoso, Klimatologi Pertanian. Bandar Lampung: Pusaka Media, 2019.
D. H. Yoranda, M. T. Furqon, and M. Data, "Prediksi Intensitas Curah Hujan Menggunakan Metode Jaringan Saraf Tiruan Backpropagation," J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 10, pp. 3793–3801, 2018.
N. Tyagi and A. Kumar, "Neural Network on Monthly Rainfall Prediction," 2006.
A. Samad, Bhagyanidhi, V. Gautam, P. Jain, Sangeeta, and K. Sarkar, "An Approach for Rainfall Prediction Using Long Short Term Memory Neural Network," 2020 IEEE 5th Int. Conf. Comput. Commun. Autom. ICCCA 2020, pp. 190–195, 2020, doi: 10.1109/ICCCA49541.2020.9250809.
I. Wahyuni, N. R. Adam, W. F. Mahmudy, and A. Iriany, "Modeling backpropagation neural network for rainfall prediction in tengger east Java," Proc. - 2017 Int. Conf. Sustain. Inf. Eng. Technol. SIET 2017, vol. 2018-January, pp. 170–175, 2018, doi: 10.1109/SIET.2017.8304130.
M. T. P. Manalu, "Jaringan Syaraf Tiruan untuk Memprediksi Curah Hujan Sumatera Utara dengan Metode Back Propagation (Studi Kasus : BMKG Medan)," JURIKOM (Jurnal Ris. Komputer), vol. 3, no. 1, pp. 35–40, 2016.
N. Ritha, M. Bettiza, and A. Dufan, "Prediksi Curah Hujan dengan Menggunakan Algoritma Levenberg-Marquardt dan Backpropagation," J. Sustain., vol. 5, no. 2, pp. 11–16, 2016.
B. D. A. N. Levenberg-marquardt, "Prediksi Curah Hujan Kota Semarang Dengan Feedforward Neural Network Menggunakan Algoritma Quasi Newton Bfgs Dan Levenberg-Marquardt," J. Presipitasi, vol. 3, no. 2, pp. 46-52–52, 2007, doi: 10.14710/presipitasi.v3i2.46-52.
M. Kafil, "Penerapan Metode K-Nearest Neighbors Untuk Prediksi Penjualan Berbasis Web Pada Boutiq Dealove Bondowoso," JATI (Jurnal Mhs. Tek. Inform., vol. 3, no. 2, pp. 59–66, 2019, doi: 10.36040/jati.v3i2.860.
F. Rohmawati, M. G. Rohman, and S. Mujilahwati, "Sistem Prediksi Jumlah Pengunjung Wisata Wego Kec.Sugio Kab.Lamongan Menggunakan Metode Fuzzy Time Series," Jouticla, vol. 2, no. 2, 2017, doi: 10.30736/jti.v2i2.66.
Dinas Pengelolaan Sumber Daya Air, "Geografis Kota Sukabumi," 2013. [Online]. Available: https://portal.sukabumikota.go.id/geografis/.
A. Sharaff and S. R. Roy, "Comparative Analysis of Temperature Prediction Using Regression Methods and Back Propagation Neural Network," Proc. 2nd Int. Conf. Trends Electron. Informatics, ICOEI 2018, no. Icoei, pp. 739–742, 2018, doi: 10.1109/ICOEI.2018.8553803