Ant Colony – Information Gain based Feature Selection Method for Weather Dataset

Abstract

Weather forecasting is an emerging domain that predicts the weather condition at a location at a time. Weather forecasting is considered as the most sensitive research field which facing a lot of real-time issues such as inaccurate prediction, lack of handling in huge data volume and inadequate in technology advancement.  Forecasting weather conditions is important for, e.g., operation of hydro power plants and for flood management. Mechanistic models are known to be computationally demanding. Hence, it is of interest to develop models that can predict weather conditions faster than traditional meteorological models. The field of machine learning has received much interest from the scientific community. Due to its applicability in a variety of fields, it is of interest to study whether an artificial neural network can be a good candidate for prediction of weather conditions in combination with large data sets. The availability of meteorological data from multiple online sources is an advantage. In this work, an Ant Colony – Information Gain Based Feature Selection method is proposed using optimization technique and filter-based feature selection method.