A TIME-DELAY CASCADING NEURAL NETWORK ARCHITECTURE FOR MODELING TIME-DEPENDENT PREDICTOR IN ONSET PREDICTION

Abstract

The occurrence of rain before the real start of a rainy season often mislead farmers into thinking that rainy season has started and suggesting them to start planting immediately. In reality, rainy season has not started yet, causing the already-planted rice seed to experience dehydration. Therefore, a model that can predict the onset of rainy season is required, so that draught disaster can be avoided. This study presents Time Delay-Cascading Neural Network (TD-CNN) which deals with situations where the response variable is determined by a number of time-dependent inter-related predictors. The proposed model is used to predict the onset in Pacitan District Indonesia based on Southern Oscillation Index (SOI). The Leave One Out (LOO) cross-validation with series data 1982-2012 are used in order to compare the accuracy of the proposed model with the Back-Propagation Neural Network (BPNN) and Cascading Neural Network (CNN). The experiment shows that the accuracy of the proposed model is 0.74, slightly above than the two other models, BPNN and CNN which are 0.71 and 0.72, respectively.