Performance of Clustering on ANFIS for Weather Forecasting

This paper proposes the comparison of using K-Means and Fuzzy C-Means (FCM) to optimize the premise parameters on Adaptive Neuro-Fuzzy Inference System (ANFIS) for weather forecasting. The ANFIS architecture groups each of the feature inputs in the first layer into three clusters, and uses three rules for the second layer. The comparison is performed based on the RMSE value and the number of iteration. The testing is done on the percentage of 40%, 50%, and 60% of the total data. In addition, the testing is done by grouping the data based on season called rainy and dry seasons. The testing results show that both K-Means and FCM have almost the same RMSE, except for rainy season where K-Means has better RMSE. However, K-Means requires relatively more iterations to achieve convergence. The use of FCM, in general, gives better results than K-Means. It is also shown that ANFIS provides the best performance for data onto the dry season.