Weather Forecasting Using Soft Computing 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 Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) models were used to analyze metrological data sets obtained from the metrological station. The data covers a five year period (2008-2012) were for the monthly means of minimum and maximum temperature, rainfall, wind run, 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 MLP ANN model with a lower prediction error.