(University of Sriwijaya, Indonesia)(2) Azhar Kholiq Affandi
(University of Sriwijaya, Indonesia)(3) * Iskhaq Iskandar
(University of Sriwijaya, Indonesia)(4) Yosi Apriani
(Universitas Muhammadiyah Palembang, Indonesia)*corresponding author
AbstractTwo models of Artificial Neural Network (ANN) algorithm have been developed for monthly rainfall prediction, namely the Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN). A total data of 238 months (1994-2013) was used as the input data, in which 190 data were used as training data and 48 data used as testing data. Rainfall data has been tested using architecture BPNN with various learning rates. In addition, the rainfall data has been tested using the RBFNN architecture with maximum number of neurons K = 200, and various error goals. Statistical analysis has been conducted to calculate R, MSE, MBE, and MAE to verify the result. The study showed that RBFNN architecture with error goal of 0.001 gives the best result with a value of MSE = 0.00072 and R = 0.98 for the learning process, and MSE = 0.00092 and R = 0.86 for the testing process. Thus, the RBFNN can be set as the best model for monthly rainfall prediction.
KeywordsPrediction; Rainfall; BPNN; RBFNN
|
DOIhttps://doi.org/10.26555/ijain.v4i2.208 |
Article metricsAbstract views : 2491 | PDF views : 513 |
Cite |
Full Text Download
|
References
[1] M. Zikra, Suntoyo, and Lukijanto, “Climate Change Impacts on Indonesian Coastal Areas,” Procedia Earth Planet. Sci., vol. 14, pp. 57–63, 2015, doi: https://doi.org/10.1016/j.proeps.2015.07.085.
[2] H. Meinke et al., “Rainfall variability of decadal and longer time scales: Signal or noise?,” J. Clim., vol. 18, no. 1, pp. 89–90, 2005, doi: https://doi.org/10.1175/JCLI-3263.1.
[3] L. Tacconi, P. F. Moore, and D. Kaimowitz, “Fires in tropical forests - What is really the problem? Lessons from Indonesia,” Mitig. Adapt. Strateg. Glob. Chang., vol. 12, no. 1, pp. 55–66, 2007, doi: https://doi.org/10.1007/s11027-006-9040-y.
[4] M. Ardiansyah, R. Boer, and A. P. Situmorang, “Typology of land and forest fire in South Sumatra, Indonesia Based on Assessment of MODIS Data,” in IOP Conference Series: Earth and Environmental Science, 2017, vol. 54, doi: https://doi.org/10.1088/1755-1315/54/1/012058.
[5] A. Saxena, N. Verma, and K. C. Tripathi, “Neuro-genetic hybrid approach for rainfall forecasting,” Int. J. Comput. Sci. Inf. Technol., vol. 5, no. 2, pp. 1291–1295, 2014, available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.639.5409&rep=rep1&type=pdf.
[6] Haviluddin and I. Tahyudin, “Time series prediction using radial basis function neural network,” Int. J. Electr. Comput. Eng., vol. 5, no. 4, pp. 765–771, 2015, available at: https://www.iaescore.com/journals/index.php/IJECE/article/view/5671.
[7] K. W. Wong, P. M. Wong, T. D. Gedeon, and C. C. Fung, “Rainfall prediction model using soft computing technique,” Soft Comput., vol. 7, no. 6, pp. 434–438, 2003, doi: https://doi.org/10.1007/s00500-002-0232-4.
[8] M. N. French, W. F. Krajewski, and R. R. Cuykendall, “Rainfall forecasting in space and time using a neural network,” J. Hydrol., vol. 137, no. 1–4, pp. 1–31, 1992, doi: https://doi.org/10.1016/0022-1694(92)90046-X.
[9] M. Richard and K. G. Rao, “Artificial neural networks in temporal and spatial variability studies and prediction of rainfall,” ISH J. Hydraul. Eng., vol. 20, no. 1, pp. 1–6, 2014, doi: https://doi.org/10.1080/09715010.2013.806400.
[10] U. Yolcu, E. Egrioglu, and C. H. Aladag, “A new linear & nonlinear artificial neural network model for time series forecasting,” Decis. Support Syst., vol. 54, no. 3, pp. 1340–1347, 2013, doi: https://doi.org/10.1016/j.dss.2012.12.006.
[11] A. K. Jain, J. Mao, and K. M. Mohiuddin, “Artificial Neural Network: A Tutorial,” Communications, vol. 29, pp. 31–44, 1996, doi: https://doi.org/10.1109/2.485891.
[12] K. Abhishek, M. P. Singh, S. Ghosh, and A. Anand, “Weather Forecasting Model using Artificial Neural Network,” Procedia Technol., vol. 4, pp. 311–318, 2012, doi: https://doi.org/10.1016/j.protcy.2012.05.047.
[13] Mislan, Haviluddin, S. Hardwinarto, Sumaryono, and M. Aipassa, “Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia,” Procedia Comput. Sci., vol. 59, no. Iccsci, pp. 142–151, 2015, doi: https://doi.org/10.1016/j.procs.2015.07.528.
[14] I. Wahyuni, N. R. Adam, W. F. Mahmudy, and A. Iriany, “Modeling Backpropagation Neural Network for Rainfall Prediction in Tengger East Java,” 2nd Int. Conf. Sustain. Inf. Eng. Technol. (SIET 2017), no. 11, pp. 170–175, 2017, doi: https://doi.org/10.1109/SIET.2017.8304130.
[15] S. C. Joshi and A. N. Cheeran, “MATLAB based back-propagation neural network for automatic speech recognition,” Int. J. Adv. Res. Electr. Electron. Instrum. Eng., vol. 3, no. 7, pp. 10498–10504, 2014, doi: https://doi.org/10.15662/ijareeie.2014.0307016.
[16] Y. U. Zhijun, “RBF neural networks optimization algorithm and application on tax forecasting,” Telkomnika, vol. 11, no. 7, pp. 3491–3497, 2013, doi: https://doi.org/10.11591/telkomnika.v11i7.2199.
[17] H. Q. Zhang and J. B. Li, “Prediction of tourist quantity based on RBF neural network,” J. Comput., vol. 7, no. 4, pp. 965–970, 2012, doi: https://doi.org/10.4304/jcp.7.4.965-970.
[18] M. Awad, H. Pomares, I. Rojas, O. Salameh, and M. Hamdon, “Prediction of tme series using RBF neural networks: a new approach of clustering.,” Int. Arab J. Inf. …, vol. 6, no. 2, pp. 138–144, 2009, available at: http://www.ccis2k.org/iajit/PDF/vol.6,no.2/5PTSURNNNAC138.pdf.
[19] Haviluddin and A. Jawahir, “Comparing of ARIMA and RBFNN for short-term forecasting,” Int. J. Adv. Intell. Informatics, vol. 1, no. 1, pp. 15–22, 2015, doi: https://doi.org/10.1292/ijain.v1i1.10.g8.
[20] A. Agustin, W. Mardiansyah, D. Setiabudidaya, and I. Iskandar, “WindSat and RAMA Buoy: a comparison of ocean-atmosphere data,” in MATEC Web of Conferences, 2017, vol. 101, doi: https://doi.org/10.1051/matecconf/201710104005.
[21] A. Comrie, “Comparing neural networks and regression models for ozone forecasting,” J. Air Waste Manag. Assoc., vol. 47, no. 6, pp. 653–663, 1997, doi: https://doi.org/10.1080/10473289.1997.10463925.
[22] D. G. Fox, “Judging air quality model performance,” 1981, vol. 62, no. 5, pp. 599–609, doi: https://doi.org/10.1175/1520-0477(1981)062<0599:JAQMP>2.0.CO;2.
[23] M. Kolehmainen, H. Martikainen, and J. Ruuskanen, “Neural networks and periodic components used in air quality forecasting,” Atmos. Environ., vol. 35, no. 5, pp. 815–825, 2001, doi: https://doi.org/10.1016/S1352-2310(00)00385-X.
[24] E. Walker, L. H. Slørdal, C. Guerreiro, F. Gram, and K. E. Grønskei, “Air pollution exposure monitoring and estimation. Part II: model evaluation and population exposure,” J. Environ. Monit., vol. 1, no. 4, pp. 321–326, 1999, doi: https://doi.org/10.1039/A902776I.
[25] C. J. Willmott et al., “Statistics for the evaluation and comparison of models,” J. Geophys. Res. Ocean., vol. 90, no. C5, pp. 8995–9005, 1985, doi: https://doi.org/10.1029/JC090iC05p08995.
[26] C. . Willmott, “Some comments on the evaluation of model performance,” 1982, vol. 63, no. 11, pp. 1309–1313, doi: https://doi.org/10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0