Predictive Mining of Rainfall Predictions Using Artificial Neural Networks for Chao Phraya River

Abstract- The rainfall is one of the significant data set of water resource management. With the monthly historical rainfall data in the period of 1941-1999 form 245 rainfall monitor stations in Thailand around Chao Phraya River, the rainfall prediction with an artificial intelligent technique is possible. Artificial neural networks is one the most widely supervised techniques of data mining. It can be applied on predictive mining tasks to make a prediction. The main contribution of this paper is to utilize a neural network model for monthly rainfall prediction. The training and testing patterns are prepared as a time-series data of the past ten months. The numbers of training and testing patterns are 372 and 96, respectively. In the training step, the neural network gives 99.6 % of accuracy and 96.9 % of accuracy in the testing step. The results show that it is possible to predict annual rainfall one year ahead with acceptably accuracy