Climatic Prognosticate Identification using Soft Computing Techniques and Algorithmic Concepts

Abstract

Climatic prognosticates is the application of recent technology to forebode the state of the troposphere for a climate time at a given section. It is implementing by compile quantitative testimony about the current state of the atmosphere and past and/or present experiences. In this study flexible Adaptive Neuro-Fuzzy Inference System (ANFIS) and multiple linear regression models were used to inspect barometrical testimony sets access from the barometrical station. The Multiple linear regression models is elementary due to the actuality that it uses elementary algorithmic equation using Multiple Linear Regression (MLR) equations that can be simply understood by a medium educated farmer. Adaptive Neuro- Fuzzy Inference Systems (ANFIS) combines the capabilities of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) to solve different kinds of problems. The testimony 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.