Determining the Neuron Weights of Fuzzy Neural Networks Using Multi-Populations Particle Swarm Optimization for Rainfall Forecasting
Keywords:Multi-Population, Particle Swarm Optimization, Rainfall Forecasting, Time-series Forecasting,
AbstractRainfall trends forecasting is essential for several fields, such as airline and ship management, flood control and agriculture and it can be solved by Fuzzy Neural Networks (FNN) approach. However, one of the challenges in implementing the FNN algorithm is to determine the neuron weights. In comparison to Gradient Descent approach, Particle Swarm Optimization (PSO) has been the common approach used to determine neuron weights that result in a more accurate output. However, one of the weaknesses of PSO approach is it tends to convergence after iteration. To overcome this weakness, this study uses a multi-population mechanism to improve the result of PSO approach. The result shows that FNN optimized by PSO with the multi-population mechanism provided a better result than FNN optimized by standard PSO approach and by Gradient Descent approach. Besides, FNN optimized by PSO with multi-population mechanism is capable to produce a better result than the standard Multi-layer Neural Networks optimized by PSO.
J. Patel and F. Parekh, “Forecasting Rainfall Using Adaptive NeuroFuzzy Inference System (ANFIS),” Int. J. Appl. Innov. Eng. Manag., vol. 3, no. 6, pp. 262–269, Jun. 2014.
W. T. Zaw and T. T. Naing, “Modeling of Rainfall Prediction Over Myanmar using Polynomial Regression,” presented at the International Conference on Computer Engineering and Technoloogy (ICCET)., 2009, pp. 316–320.
P. Guhathakurta, “Long-range monsoon rainfall prediction of 2005 for the districts and sub-division Kerala with artificial neural network,” Curr. Sci., vol. 90, no. 6, pp. 773–779, 2006.
L. V. Fausett, Fundamentals Of Neural Network: Architecture, Algorithms, and Applications, International Editions. Prentice-Hall, 1994.
D. S. Wilks, “Multisite generalization of a daily stochastic precipitation generation model,” J. Hydrol., vol. 210, no. 1, pp. 178– 191, 1998.
A. Iriany, W. F. Mahmudy, S. Handoyo, A. D. Sulistyono, and S. K. Nisak, “GSTAR-SUR Model for Rainfall Forecasting in Tengger Region, East Java,” in Planning for Environmental Sustainability for the Well Being of Future Humanity, Malang, Indonesia, 2015.
S. Zhao and L. Wang, “Support Vector Regression Based on Particle Swarm Optimization for Rainfall Forecasting,” presented at the 3rd International Joint Conference on Computational Science and Optimization (CSO), 2010, pp. 484–487.
M. C. C. Utomo and W. F. Mahmudy, “Optimization of Fuzzy’s Rules for Rainfall Forecasting using Particle Swarm Optimization,” Int. J. Eng. Inform., no. 2016.
J. B. Sulaiman, H. Darwis, and H. Hirose, “Monthly Maximum Accumulated Precipitation Forecasting Using Local Precipitation Data and Global Climate Modes,” J. Adv. Intell. Intell. Inform., vol. 18, no. 6, pp. 999–1006, Nov. 2014.
F. A. Huda, W. F. Mahmudy, and H. Tolle, “Android malware detection using backpropagation neural network,” Indones. J. Electr. Eng. Comput. Sci., vol. 4, no. 1, 2016.
I. Wahyuni, P. F. E. Adipraja, W. F. Mahmudy, and A. Iriany, “Rainfall Prediction in Tengger Indonesia: A System Dynamic Approach,” Int. J. Intell. Eng. Syst., 2017.
S. Haykin, Neural Networks. A Comprehensive Foundation, 2nd ed. Singapore: Pearson Prentice Hall, 2005.
M. C. C. Utomo and W. F. Mahmudy, “Optimization of Sugeno Fuzzy Inference System’s Rules for Rainfall Forecasting,” IAENG, no. 2016.
J. Shen, W. Shen, J. Chang, and N. Gong, “Fuzzy Neural Network for Flow Estimation in Sewer Systems During Wet Weather,” Water Environ. Res., vol. 78, no. 2, pp. 100–109, Feb. 2006.
G. Corani and G. Guariso, “Coupling Fuzzy Modeling and Neural Networks for River Flood Prediction,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 35, no. 3, pp. 382–390, Aug. 2005.
K. G. Abistado and C. N. Arellano, “Weather Forecasting Using Artificial Neural Network and Bayesian Network,” J. Adv. Intell. Intell. Inform., vol. 18, no. 5, pp. 812–817, Sep. 2014.
J. B. Yabuuchi and J. Watada, “Fuzzy Autocorrelation Model with Confidence Intervals of Fuzzy Random Data,” J. Adv. Intell. Intell. Inform., vol. 18, no. 2, pp. 197–203, Mar. 2014.
J. T. Heaton, Introduction to Neural Networks for Java, 2nd ed. Heaton Research, Inc., 2008.
D. Novitasari, I. Cholissodin, and W. F. Mahmudy, “Hybridizing PSO With SA for Optimizing SVR Applied to Software Effort Estimation,” Telkomnika, vol. 14, p. 1, 2016.
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