Rainfall Prediction Using Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) and Genetic Algorithm


  • Ida Wahyuni Faculty of Computer Science, Brawijaya University, Indonesia
  • Wayan Firdaus Mahmudy Faculty of Computer Science, Brawijaya University, Indonesia
  • Atiek Iriany Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia


Prediction, Rainfall, ANFIS, Genetic Algorithm, Sugeno FIS, ANFIS-GA,


Tengger Indonesia is one of the rich areas in agricultural commodities and one of its commodities is potatoes. In the process of planting potatoes, rainfall data is used to determine the most appropriate planting time in order to harvest the maximum yield. However, the current rainy season is erratic and very difficult to predict the planting time, especially in the area of Tengger. It requires a method that can predict rainfall with the smallest error as possible. Adaptive Neuro-Fuzzy Inference System (ANFIS) is one of the prediction methods that are quite reliable because it is equipped with a network that can learn. The ANFIS uses Sugeno FIS in its architecture. To improve the prediction results, the Sugeno FIS will be optimized in boundaries of membership function and coefficient consequent rule before it goes into the process of training with ANFIS. A genetic algorithm is used for the optimization process. The results of rainfall prediction using hybrid ANFIS-GA are proven to produce smaller RMSE of rainfall prediction method that has never been done before.


L. Pervin and S. Islam, “System dynamics approach for modeling of sugar beet yield considering the effects of climatic variables,” J. Sci. Food Agric., vol. 95, pp. 515–521, 2015.

J. Batoro, D. Setiadi, and T. Chikmawati, “Pengetahuan Tentang Tumbuhan Masyarakat Tengger di Bromo Tengger Semeru Jawa Timur,” J. Wacana - J. Sos. dan Hum., pp. 1–10, 2006.

A. Iriany, W. F. Mahmudy, A. D. Sulistyono, and S. C. Nisak, “GSTAR-SUR Model for Rainfall Forecasting in Tengger Region , East Java,” 1st Int. Conf. Pure Appl. Res. Univ. Muhammadiyah Malang, 21-22 August, no. 1, pp. 1–8, 2015.

S. Indriantoro, “Dampak Perubahan Iklim Terhadap Usaha Tani Kentang Dataran Tinggi Tengger (Studi Kasus di Desa Ngadisari Kecamatan Sukapura Kabupaten Probolinggo),” Agribisnis, pp. 1–8, 2010.

I. Wahyuni, W. F. Mahmudy, and A. Iriany, “Rainfall Prediction in Tengger Region-Indonesia Using Tsukamoto Fuzzy Inference System,” 1th Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng., pp. 1–11, 2016.

I. Wahyuni and W. F. Mahmudy, “Rainfall Prediction in Tengger-Indonesia Using Hybrid Tsukamoto FIS and Genetic Algorithm,” Submited to J. ICT Res. Appl., pp. 1–8, 2016.

K. Li and H. Su, “Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive networkbased fuzzy inference system,” Energy Build., vol. 42, no. 11, pp. 2070–2076, 2010.

S. H. Kusumadewi, “Fuzzy Multi-Attribute Decision Making (Fuzzy MADM),” Graha Ilmu Yogyakarta, p. 2006, 2006.

A. W. Jayawardena, E. D. P. Perera, B. Zhu, J. D. Amarasekara, and V. Vereivalu, “A comparative study of fuzzy logic systems approach for river discharge prediction,” J. Hydrol., vol. 514, pp. 85–101, 2014.

G. D. Santika, W. F. Mahmudy, and A. Naba, “Electrical Load Forecasting using Adaptive Neuro-Fuzzy Inference System,” Int. J. Adv. Soft Comput. its Appl., pp. 1–20, 2017.

J.-S. R. Jang, “Input selection for ANFIS learning,” Proc. IEEE 5th Int. Fuzzy Syst., vol. 2, pp. 1493–1499, 1996.

R. Singh, A. Kainthola, and T. N. Singh, “Estimation of elastic constant of rocks using an ANFIS approach,” Appl. Soft Comput. J., vol. 12, no. 1, pp. 40–45, 2012.

J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665–685, 1993.

Y. Ding and X. Fu, “Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm,” Neurocomputing, vol. 188, pp. 233–238, 2015.

W. Mahmudy, R. Marian, and LHS Luong, “Real coded genetic algorithms for solving flexible job-shop scheduling problem – Part II: optimization,” Adv. Mater. Res., vol. 701, pp. 364–369, 2013.

W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Hybrid Genetic Algorithms for Multi-Period Part Type Selection and Machine Loading Problems in Flexible Manufacturing System,” IEEE Int. Conf. Comput. Intell. Cybern. Yogyakarta, Indones. 3-4 December, pp. 126–130, 2013.

I. Wahyuni and F. Utaminingrum, “Error Numerical Analysis for Result of Rainfall Prediction Between Tsukamoto FIS and Hybrid Tsukamoto FIS with GA,” 8th Int. Conf. Adv. Comput. Sci. Inf. Syst., pp. 1–8, 2016.

T. Murata and H. Ishibuchi, “Adjusting membership functions of fuzzy classification rules by genetic algorithms,” Proc. 1995 IEEE Int. Conf. Fuzzy Syst. Int. Jt. Conf. Fourth IEEE Int. Conf. Fuzzy Syst. Second Int. Fuzzy Eng. Symp., vol. 4, pp. 1819– 1824, 1995.

J. Grefenstette, “Optimization of Control Parameters for Genetic Algorithms,” IEEE Trans. Syst. Man. Cybern., vol. 16, no. 1, pp. 122–128, 1986.

W. F. Mahmudy, “Optimization of Part Type Selection and Loading Problem with Alternative Production Plans in Flexible Manufacturing System using Hybrid Genetic Algorithms – Part 2 : Genetic Operators and Results,” 2013 5th Int. Conf. Knowl. Smart Technol. Optim., pp. 81–85, 2013.

J. Jafarian, “An Experiment to Study Wandering Salesman Applicability on Solving the Travelling Salesman Problem based on Genetic Algorithm,” Int. Conf. Educ. Inf. Technol. (ICEIT 2010) An, no. Iceit, pp. 1–7, 2010.




How to Cite

Wahyuni, I., Mahmudy, W. F., & Iriany, A. (2017). Rainfall Prediction Using Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) and Genetic Algorithm. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-8), 51–56. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2627

Most read articles by the same author(s)

1 2 > >>