Technique and Decision Algorithm for Meteorological Factors Reduction in Agriculture Using Discernibility Function

Abstract

Meteorology is a branch of Atmospheric Science, which mainly focuses on predicting weather and climate, and studying the forces that bring about change in our environment.  Climate is a long term pattern of weather conditions for a given area. Climate change refers to a statistically significant variation in either the mean state of the climate or its variability, persisting for an extended period. As the issue of Global warming is heating up and natural disasters have become frequent, environmental challenges have become more and more popular with implications for agriculture and health. There is need to develop some technique for screening meteorological factors, so that the agricultural output should be increase. To achieve this motive, technique and decision algorithm for factor reduction using Rough Set Theory (RST) is studied, which is used to reduce the dimensionality of the dataset without loss of generality.