Potential of k-Means Clustering-Based Fuzzy Logic for Prediction of Temperature in Ambient Atmosphere

Abstract

Accurate prediction of meteorological parameters is a challenging task due to dynamic nature of atmosphere. It has drawn a lot of research potential. Among this, temperature prediction has prominent applications in many important sectors like agriculture, vegetation, water resources, tourism, energy, aviation, etc. Temperature prediction is quite complex in ambient atmosphere, and stochastic models have the limitations of not able to learn the nonlinear relationships between the considered variables. The fuzzy logic can express uncertain and inaccurate information without involvement of physical processes. An attempt is made in this paper to present a k-means clustering approach for future temperature prediction using fuzzy logic. The database of maximum temperature, corresponding M.S.L. pressure, relative humidity, wind speed and historical temperature are utilized to develop a prediction methodology in fuzzy rule base domain to estimate next day maximum temperature for Mumbai, India, in the study. Clusters of input and output parameters are made using k-means clustering, and based on this, fuzzy knowledge base is prepared for prediction of temperature. The model is able to predict the temperature with lower prediction errors.