Recurrent gradient descent adaptive learning rate and momentum neural network for rainfall forecasting

This paper presents a recurrent optimized heuristic Artificial Neural Network (ANN) uses Recurrent Elman Gradient Descent Adaptive Learning Rate and Momentum method with various parameter values. The values is approached by using El-Nino Southern Oscillation (ENSO) variable, which are Southern Oscillation Index (SOI), Wind, Outgoing Long Wave Radiation (OLR), and Sea Surface Temperature (SST) to forecast regional monthly rainfall in Bongan Bali. The data sets used in this paper are separated by two groups. First group of data has training data and testing data which is 75% and 25% respectively. The latter group of data consists of 50% training data and 50% testing data. First data group produces the maximum R
²
leap 74.6% and the latter produces the maximum R
²
leap 49.8%.