Forecasting the length of the rainy season using time delay neural network

Indonesia has abundant natural resources in agriculture. Good agricultural results can be obtained by determining a good growing season plan. One of important factors which determines the successful of crop is the length of the rainy season. The length of the rainy season is dynamic and difficult to be controlled. Therefore the planning of the growing season becomes inaccurate and cause crop failures. This research aims to develop a model to predict the length of the rainy season using time delay neural network (TDNN). Observational data used in this research is the length of rainy season from three weather and climate stations of the Pacitan region from 1982/1983 to 2011/2012. Predictor data used in this reserach is sea surface temperature (SST) derived from the region of Nino 1+2, Nino 3, Nino 4, and Nino 3.4 from 1982 to 2011. Model with the best accuracy was obtained by Pringkuku station with RMSE of 1.97 with parameters of delay [0 12 3], learning rate 0.1, 40 hidden neurons, and predictors of Nino 3 and R-squared of 0.82 with parameters of delay [0 1], learning rate 0.3, 5 hidden neurons, and predictors of Nino 3.