Introduction on Rainfall Prediction methods

Human have had a long excitement to discover various new things and one of them is to predict weather. Human mind was occupied in search of finding a way to predict weather such as rainfall prediction. But predicting future is not possible by anyone so people have used different kinds of machinery and tried to predict weather such as rainfall. Accurate forecasting of rainfall has been one of the most important issues in hydrological research. It helps to improve the daily lives or basis by different meanings. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. Many machine learning approaches were proposed in relation to rainfall forecasting. The machine learning techniques utilized for rainfall prediction includes Artificial Neural Network(ANN).


 ANN is an interconnected group of nodes which is inspired by a simplification of neurons of brain. It is a computing system that constitute animal brains. Systems like this learn to perform tasks by considering examples. It is a network consisting of an arbitrary number of very simple elements called nodes. Each node is a simple processing element that responds to the weighted inputs it receives from other nodes (Lee et al., 2004)

Fuzzy logic is another technique which is used to forecast weather prediction. Fuzzy logic is a method of reasoning that resemble human reasoning. As a computer works in a precise as YES or NO fuzzy logic does no work as that. Fuzzy logic works as a possibility of such as certainly yes, possibly yes,possibly no,certainly no. And fuzzy logic works on the levels of possibilities of input to achieve the definite output. Fuzzy logic's approach to control problems mimics how a person would make decisions, only much faster.