A monsoon onset and offset prediction model using backpropagation and moron method: A case in drought region

Abstract

First day (onset) and last day (offset) of monsoon are nature phenomena which are important elements at cultivation stages in agriculture. These 2 sets of time value influent harvest performance but it is difficult to predict onset and offset at drought region. One of technique that can be used to solve mentioned problem is prediction technique which is one of data mining task. In this research, Feed Forward Backpropagation (BPNN) were combined with Moron method to predict onset and offset at drought region. Data used were daily rainfall data from 1983 to 2013. This experiment used 2 kind of BPNN models and they used S different values for learning rate (alpha) from range 0.01 to 0.2. Root Mean Square Error (RMSE) is used to evaluate resulted prediction models along with correlation value and standard deviation of error for better understanding. For BPNN onset model, lowest RMSE value at alpha 0.15 is 32,0546 and lowest RMSE value for BPNN offset is 26,6977 at alpha 0.05. Developed model has been able to use for prediction, but the result was still not close enough to actual data. In order to achieve a better model with lower RMSE, it is neccesary to improve model architecture and to specify some methods to obtain certain number of input layer based on Southern Oscillation Index (SOI) data.