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 mjslcad farmers imo thinking lha1 rainy season has started and suggesting lhcm to start planting immediately. In reality, rainy season has not started yet, cau:.-ing Lhe already-planted rice seed to experience dehydration. Therefore, a model that can predict the onset of rainy season is required, so lhat drought disaster can be avoided. This study presents Time DelayCascading Neural Network (TD-CNN) which deals with situations where lhe response variable is determined by a number of time-dependent inter-related predictors. The proposed model is used to predict lhe onset in Pacitan District Indonesia based on Southern Oscillation Index (SOI). The Leave One Out (LOO) cros~-validation w11h i.erie.~ data 1982-2012 are used in order to compare the accuracy of the proposed model with lhe Back-Propagation Neural Network (BPNN) and Cascading Neurnl Network (CNN). The experiment shows that lhe accuracy or the proposed model is 0.74, slightly above than the two other models, BPNN and CNN which a.re 0.71 and 0.72. respectively.