Time Series Data Mining in Rainfall Forecasting Using Artificial Neural Network

Abstract: Rainfall is very important parameter in hydrological model. Many techniques and models have been developed for rainfall time series prediction. In this study an artificial neural network (ANN) based model was developed for rainfall time series forecasting. Proposed model used Multilayer perceptron (MLP) network with back propagation algorithm for training. Discharge and rainfall data are took as input parameter for ANN model to predict rainfall time series. Data preprocessing and model’s sensitivity analysis were executed. Collected data is divided in three sets for optimal neural network training. The first set is the training set, used for calculate the gradient and updating the network weights and biases. The second set is the validation set. The error on the validation set is follow during the training process. The third set is test set. It is used to compare different models. Different topologies of Neural Networks were created with change in hidden layer, number of processing element and activation function. Mean Absolute error (MAE), Mean Squared error (MSE) and correlation coefficient (CC) are used to evaluate the model performance. On the basis of these evaluation parameter results, it is found that multilayer perceptron (MLP) network predict more accurate than other traditional models