APPLICATION OF MULTIVARIATE ANFIS FOR DAILY RAINFALL PREDICTION: INFLUENCES OF TRAINING DATA SIZE

This study investigates the use of multi variable Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting daily rainfall using several surface weather parameters as predictors. The data used in this study comes from automatic weather station data collected in Timika airport from January until July 2005 with 15-minute time interval. We found out that relative humidity is the best predictor with a stable performance regardless of training data size and low RMSE amount especially in comparison to those from other predictors. Other predictors shows no consistent performances with different training data size. Performances of ANFIS reach a slightly above 0.6 in correlation values for daily rainfall data without any filtering for up to 100 data in a time series. The performance of ANFIS is sensitive to the magnitude and scale differences among predictors, thus suggesting introducing a transforming and scaling factor or functions. Application of multivariate ANFIS is relatively new in Indonesia. However, results presented here indicate some promises and possible roadmaps for improvements.