Development of Tournament Selection of Genetic Algorithm for Forecasting Rainfall with Artificial Neural Network
Keywords:
Tournament selection, Genetic algorithm, Artificial neural network, Rainfall forecastingAbstract
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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