Rainfall Forecasting Using Artificial Neural Network: A Data Mining Approach

Rainfall forecasting or Weather forecasting has been one of the most challenging problems around the world because it consists of multidimensional and nonlinear data such as in the field of agriculture to determine initial growing season. Recently, climate change causes much trouble in rainfall forecasting. This paper describes five data mining algorithms namely neural network (NN), random forest, classification and regression tree (CRT), support vector machine (SVM) and k-nearest neighbour. Generally these algorithms are used for the prediction. In this paper rainfall forecasting using Artificial Neural Network (ANN) and Genetic Algorithm ( GA) is made. In genetic algorithm we use Hidden Markov Model (HMM) for records the previous data. The data used within this research is taken from Yahoo Weather API is the type of Interface. Those data include temperature, air pressure, rainfall, relative humidity, and wind speed. Based on experiment result, it can be concluded that the combination of GA and HMM weather data can gives prediction graph with higher than 90 % accuracy with several population size and crossover probability