Rainfall Prediction in Tengger, Indonesia Using Hybrid Tsukamoto FIS and Genetic Algorithm Method

Ida Wahyuni, Wayan Firdaus Mahmudy


Countries with a tropical climate, such as Indonesia, are highly dependent on rainfall prediction for many sectors, such as agriculture, aviation, and shipping. Rainfall has now become increasingly unpredictable due to climate change and this phenomenon also affects Indonesia. Therefore, a robust approach is required for more accurate rainfall prediction. The Tsukamoto Fuzzy Inference System (FIS) is one of the algorithms that can be used for prediction problems, but if its membership functions are not specified properly, the prediction error is still high. To improve the results, the boundaries of the membership functions can be adjusted automatically by using a genetic algorithm. The proposed genetic algorithm employs two selection processes. The first one uses the Roulette wheel method to select parents, while the second one uses the elitism method to select chromosomes for the next generation. Based on this approach, a rainfall prediction experiment was conducted for Tengger, Indonesia using historical rainfall data for ten-year periods. The proposed method generated root mean square errors (RMSE) of 6.78 and 6.63 for the areas of Tosari and Tutur respectively. These results are better compared with the results using Tsukamoto FIS and the Generalized Space Time Autoregressive (GSTAR) model from previous studies.


genetic algorithm; hybrid; prediction; rainfall; roulette wheel; Tsukamoto FIS.

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Annas, S., Kanai, T. & Koyama, S., Assessing Daily Tropical Rainfall Variations using a Neuro-Fuzzy Classification Model, Ecological Informatics, 2(2), pp. 159-166, 2007.

Kashid, S.S. & Maity, R., Prediction of Monthly Rainfall on Homogeneous Monsoon Regions of India based on Large Scale Circulation Patterns using Genetic Programming, J. Hydrol., 454-455, pp. 26-41, 2012.

Chang, F.J., Chiang, Y.M., Tsai, M.J., Shieh, M.C., Hsu, K.L. & Sorooshian, S., Watershed Rainfall Forecasting using Neuro-fuzzy Networks with the Assimilation of Multi-sensor Information, J. Hydrol., 508, pp. 374-384, 2014.

Iriany, A., Mahmudy, W.F., Sulistyono, A.D. & Nisak, S.C., GSTAR-SUR Model for Rainfall Forecasting in Tengger Region, East Java, 1st Int. Conf. Pure Appl. Res. Univ. Muhammadiyah Malang, 21-22 August, 1, pp. 1-8, 2015.

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

Mahmudy, W.F., Marian, R.M. & Luong, L.H.S., Hybrid Genetic Algorithms for Multi-Period Part Type Selection and Machine Loading Problems in Flexible Manufacturing System, IEEE Int. Conf. Comput. Intell. Cybern. Yogyakarta, Indonesia. 3-4 December, 13(12), pp. 126-130, 2013.

Zhang, H., Wang, F. & Zhang, B.O., Genetic Optimization of Fuzzy Membership Functions, Proc. 2009 Int. Conf. Wavelet Anal. Pattern Recognition, Baoding, July, 9(7), pp. 465-470, 2009.

Cazarez-Castro, N.R., Aguilar, L.T. & Castillo, O., Fuzzy Logic Control with Genetic Membership Function Parameters Optimization for the Output Regulation of a Servomechanism with Nonlinear Backlash, Expert Syst. Appl., 37(6), pp. 4368-4378, 2010.

Cheng, C.T., Ou, C.P. & Chau, K.W., Combining a Fuzzy Optimal Model with a Genetic Algorithm to Solve Multi-objective Rainfall-runoff Model Calibration, J. Hydrol., 268(1-4), pp. 72-86, 2002.

Sasmito, G.W. & Somantri, O., Tsukamoto Method in Decision Support System for Realization of Credit on Cooperative, 4th ICIBA 2015, Int. Conf. Inf. Technol. Eng. Appl. Palembang, 4, pp. 39-44, 2015.

Sari, N.R. & Mahmudy, W.F., Fuzzy Inference Tsukamoto System for Determining Eligibility for Prospective Employees, Semin. Nas. Sist. Inf. Indones. 2-4 Nop. 2015, No. 2002, pp. 2-4, 2015. (Text in Indonesian)

Ding, Y. & Fu, X., Kernel-based Fuzzy C-means Clustering Algorithm based on Genetic Algorithm, Neurocomputing, 188, pp. 233-238, 2015.

Mahmudy, W., Marian, R. & Luong, L.H.S., Real Coded Genetic Algorithms for Solving Flexible Job-shop Scheduling Problem – Part I: Modeling, Adv. Mater. Res., 701, pp. 359-363, 2013.

Mahmudy, W., Marian, R. & Luong, L.H.S., Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem – Part II: Optimization, Adv. Mater. Res., 701, pp. 364-369, 2013.

Murata, T. & Ishibuchi, H., 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., 4, pp. 1819-1824, 1995.

Moradi, S.T. & Nikolaev, N.I., Optimization of Cement Spacer Rheology Model Using Genetic Algorithm, IJE Trans. A Basics, 29(1), pp. 127-131, 2016.

Mahmudy, W.F., 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., 5, pp. 81-85, 2013.

Herman, N.S., Genetic Algorithms and Designing Membership Function in Fuzzy Logic Controllers, World Congr. Nat. Biol. Inspired Comput. (NaBIC 2009), 9, pp. 1753-1758, 2009.

Jafarian, J., 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, 10, pp. 1-7, 2010.

Wahyuni, I., Mahmudy, W.F. & Iriany, A. Rainfall Prediction using Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS ) and Genetic Algorithm, J. Telecommun. Electron. Comput. Eng., 9, 2017 (Article in Press).

Grefenstette, J., Optimization of Control Parameters for Genetic Algorithms, IEEE Trans. Syst. Man. Cybern., 16(1), pp. 122–128, 1986.

Cintra, M.E., Camargo, H.A. & Monard, M.C., Genetic Generation of Fuzzy Systems with Rule Extraction using Formal Concept Analysis, Inf. Sci. (Ny)., 349-350, pp. 199-215, 2016.

Ishibuchi, H., Murata, T. & Gen, M., Performance Evaluation of Fuzzy Rule-based Classification Systems Obtained by Multi-objective Genetic Algorithms, Comput. Ind. Eng., 35(3-4), pp. 575-578, 1998.

Carvalho, J.P. & Tomé, J., Qualitative Optimization of Fuzzy Causal Rule Bases using Fuzzy Boolean Nets, Fuzzy Sets Syst., 158(17), pp. 1931-1946, 2007.

Janeela Theresa, M.M. & Joseph Raj, V., Fuzzy Based Genetic Neural Networks for The Classification of Murder Cases Using Trapezoidal and Lagrange Interpolation Membership Functions, Appl. Soft Comput. J., 13(1), pp. 743-754, 2013.

DOI: http://dx.doi.org/10.5614%2Fitbj.ict.res.appl.2017.11.1.3


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