Forecasting Indonesian Weather through Evolving Neural Network (ENN) based on Genetic Algorithm

The research was motivated by the extremely altered weather condition of Indonesia within the last two decades. In forecasting the country’s weather, recently, we have been built an improvement system of back propagation performance by using Conjugate Gradient (CG) on forecasting of air temperature and humidity in Indonesia using Artificial Neural Network (ANN). The research used weather data taken from the Indonesian Agency for Meteorology Climatology and Geophysics (BMKG). The data included air temperature, rainfall, air humidity, length of sun radiation, air pressure, and wind speed in Kemayoran Jakarta within five years from 2007 to 2012. Through this piece of research, ENN (Evolving Neural Network) algorithm was compared to ANN optimalized with CG (ANN-CG) in order to discover the accuracy of weather forecasting performance between them. Genetic Algorithm was utilized with ENN to discover the ANN’s optimal weight and architecture. The result revealed that ENN could forecast rainfall with 61,18 percents of accuracy or 38,82 percents of MAPE while ANN-CG was not able to predict the rainfall in the research location for its excessive MAPE.