Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System

• Main Authors: Gusti Ahmad Fanshuri Alfarisy, Wayan Firdaus Mahmudy
• Format: Article
• Language: English
• Published: University of Brawijaya 2017-02-01
• Series: JITeCS (Journal of Information Technology and Computer Science)
• Online Access: http://jitecs.ub.ac.id/index.php/jitecs/article/view/12

Rainfall forcasting is a non-linear forecasting process that varies according to area and strongly influenced by climate change. It is a difficult process due to complexity of rainfall trend in the previous event and the popularity of Adaptive Neuro Fuzzy Inference System (ANFIS) with hybrid learning method give high prediction for rainfall as a forecasting model. Thus, in this study we investigate the efficient membership function of ANFIS for predicting rainfall in Banyuwangi, Indonesia. The number of different membership functions that use hybrid learning method is compared. The validation process shows that 3 or 4 membership function gives minimum RMSE results that use temperature, wind speed and relative humidity as parameters.