Weather Forecasting using Soft Computing and Statistical Techniques

Weather forecasting is the application of science and technology to predict the state of the atmosphere for a future time at a given location. It is carried out by collecting quantitative data about the current state of the atmosphere and past and/or present experiences. In this study Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple linear regression model were used to analyze metrological data sets obtained from the metrological station. The Multiple linear regression model is simple due to the fact that it uses simple mathematical equation using Multiple Linear Regression (MLR) equations that can be easily understood by a medium educated farmer. Adaptive NeuroFuzzy Inference Systems (ANFIS) combines the capabilities of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) to solve different kinds of problems.The data covers a five year period (2008-2012) were for the monthly means of minimum and maximum temperature, wind speed, and relative humidity and mean sea level pressure (MSLP). The results showed that both models could be applied to weather prediction problems. The performance evaluation of the two models that was carried out on the basis of root mean square error (RMSE) showed that the ANFIS model yielded better results than the multiple linear regression (MLR) model with a lower prediction error.