Ruziicka Similarity Feature Selection Based Generalized Linear Regression Analysis For Weather Forecasting Using Big Data

Abstract

Weather forecasting is the process of finding state of atmosphere at a future time and a particular location. Few research works have been developed for weather forecasting with help of various machine learning techniques. But, prediction performance of conventional machine learning technique was not enough to accurately find weather conditions.  In order address existing issues, Ruziicka similarity Feature Selection Based Generalized Linear Regression Analysis (RSFS-GLRA) technique is proposed. RSFS-GLRA technique takes weather data from cloud server as input. The designed RSFS-GLRA technique performs feature selection and prediction process. Ruziicka Similarity-Based Feature Selection (RS-FS) process is carried out to select relevant features for performing weather forecasting. After feature selection, Generalized Linear Regression Analysis based Weather Forecasting (GLRA-WF) process predicts future weather conditions. GLRA-WF algorithm is a powerful statistical method that examines relationship between two or more variables for finding future event of weather conditions according to collected historical data. This helps RSFS-GLRA technique to perform accurate weather forecasting process with minimal time and higher accuracy. Experimental evaluation of RSFS-GLRA technique is carried out using parameters such as prediction accuracy, error rate and prediction time with respect to various numbers of weather data.