Development of Tournament Selection of Genetic Algorithm for Forecasting Rainfall with Artificial Neural Network


  • Piyatida T.Chaisuwan 0831138799
  • Poonpong Suksawang
  • Jatupat Mekparyup


Tournament selection, Genetic algorithm, Artificial neural network, Rainfall forecasting


This research objectives were to develop the tournament selection of genetic algorithm (GA) for forecasting rainfall with artificial neural network (ANN) based on 3 principles; 1) normalized geometric ranking (NGR), 2) roulette wheel selection (RWS) and 3) tournament selection (TS). The research was divided into 2 parts. The first part was the development of forecasting model under the simulation of 1,100 data with iteration 1,000 epochs, and the second part was the forecasting by model of the previous one. For the latter part, the actual data were brought to divide into 2 sets; namely training and testing data set at 77% and 23%, respectively. Then, the artificial neural network model developed in the tournament selection and the artificial neural network model using the original selection of Wang et al. (2017), in aspect of forecasting efficiency by mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square Error (RMSE), and coefficient of determination (R)2. The input variables of artificial neural network were relative humidity, wind speed, zonal wind, meridional wind, evaporation, minimum air temperature, maximum air temperature and average temperature. The output variable was rainfall of Central Thailand. The total data of 1,100 was seasonally analyzed. The results showed that the forecasting model developed by the tournament selection of genetic algorithm was more effective than the model with original selection of Wang et al. (2017) in every season.


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Asadi, S., Shahrabi, J., Abbaszadeh, P., & Tabanmehr, S. (2013). A new hybrid artificial neural networks for rainfall–runoff process modeling. Neurocomputing, 121(1), 470-480.

Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Massachusetts: Addison–Wesley Longman, Inc.

Goldberg, D. E., & Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. In Foundations of genetic algorithms. Elsevier, 1(1), 69-93.

Golic, K. (2013). Application of a Neural Network Model for Solving Prediction Problems in Business Management. Journal of Economics, Business and Management, 1(1), 146-149.

Gupta, D., Vasudev, K., & Bhattacharyya, S. (2018). Genetic algorithm optimization based nonlinear ship maneuvering control. Applied Ocean Research, 74(1), 142-153.

Houck, C. R., Joines, J., & Kay, M. G. (1995). A genetic algorithm for function optimization: a Matlab implementation. Ncsu-ie tr, 95(9), 1-10.

Joines, J. A., & Houck, C. R. (1994). On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's. In Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence: 579-584. Orlando: IEEE.

Kantanantha, N. (2012). Forecasting by Causal Methods. Engineering Journal, 4(1), 33-48.

Khadwilard, A. (2011). Application of Genetic Algorithm for Optimisation Problems. RMUTP Research Journal, 5(2), 153-163.

Meesad, P. (2012). Fuzzy Systems and Neural Network. Bangkok: Faculty of Information Technology KMUTNB.

Mekparyup, J., & Saithanu, K. (2011). Performance of Neural Networks, Multi-Layer Perceptron and Radial Basis Function, for Multivariate Quality Control Charts. Burapha Science Journal, 16(2), 97-106.

Mislan, Haviluddin, Hardwinarto, S., Sumaryono, & Aipassa, M. (2015). Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan-Indonesia. Procedia Computer Science, 59(1), 142-151.

Mohammadi, M., Lakestani, M., & Mohamed, M. (2018). Intelligent parameter optimization of Savonius rotor using Artificial Neural Network and Genetic Algorithm. Energy, 143(1), 56-68.

NOAA NCEP-NCAR. (2019). International Research Institute for Climate and Society. [Data file]. Available from International Research Institute for Climate and Society Web site,

Nontasud, K., & Wessapan, T. (2017). A Prediction Model of Electricity Consumption as Applied to Development Planning of Chiang Mai International Airport. EAU Heritage Journal Science and Technology, 11(3), 124-136.

Oladele, R., & Sadiku, J. (2013). Genetic algorithm performance with different selection methods in solving multi-objective network design problem. International Journal of Computer Applications, 70(12), 5-9.

Phatcharopaswatnagul, A., Satienpirakul, K., Sittbisuntikul, K., & Sinnarong, N. (2017). Impact of climate change to cassava production in The northeast region. Veridian E-Journal, Silpakorn University, 10(3), 2528-2540.

Phiwma, N. (2015). Developing A Model for Forecasting Trends of Matching between A Job Applications and A Computer Degree Using Artificial Neural Network. Panyapiwat Journal, 7(2), 1-16.

Prakobphol, T. (2009). Artificial Neural Networks. HCU Journal, 12(24), 73-87.

Raeisi-Vanani, H., Shayannejad, M., Soltani–Toudeshki, A.–R., Arab, M.–A., Eslamian, S., Amoushahi–Khouzani, M., Marani–Barzani, M., & Ostad–Ali–Askari, K. (2017). A Simple Method for Land Grading Computations and its Comparison with Genetic Algorithm (GA) Method. International Journal of Research Studies in Agricultural Sciences (IJRSAS), 3(8), 26-38.

Saengsawang, S. (2016). Appling of Artificial Neural Network in The Agriculture. The Journal of KMUTNB, 26(2), 319-331.

Saxena, A., Verma, N., & Tripathi, K. (2014). Neuro-genetic hybrid approach for rainfall forecasting. Int J Comput Sci Inf Technol, 5(2), 1291-1295.

Shaikh, L., & Sawlani, K. (2017). A rainfall prediction model using artificial neural network. International Journal of Technical Research and Applications, 5(2), 45-48.

Sihananto, A. N., & Mahmudy, W. F. (2017). Rainfall Forecasting Using Backpropagation Neural Network. Journal of Information Technology and Computer Science, 2(2), 66-76.

Wang, Z.-H., Gong, D.-Y., Li, X., Li, G.-T., & Zhang, D.-H. (2017). Prediction of bending force in the hot strip rolling process using artificial neural network and genetic algorithm (ANN-GA). The International Journal of Advanced Manufacturing Technology, 93(9-12), 3325-3338.

Whitley, D. (1994). A Genetic Algorithm Tutorial. Statistics and Computing, 4(2), 65–85.

Yu, F., Fu, X., Li, H., & Dong, G. (2016). Improved Roulette Wheel Selection-Based Genetic Algorithm for TSP. Paper presented at the Network and Information Systems for Computers (ICNISC). 2016 International Conference on Network and Information Systems for Computers: 136-140. Shenzhen: Atlantis Press.

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T.Chaisuwan, P., Suksawang, P., & Mekparyup, J. (2020). Development of Tournament Selection of Genetic Algorithm for Forecasting Rainfall with Artificial Neural Network. Princess of Naradhiwas University Journal, 12(3), 245–261. Retrieved from