Weather Forecasting Framework for Time Series Data using Intelligent Learning Models

Weather prediction is an important field in meteorology which has to be solved most accurately. Weather forecasting has been one of the leading challenging issues across the world. Forecasting the weather is an essential need to avoid the worst consequences faced by humans due to climatic issues. Several weather predictions like thunderstorms, rainfall, predicting cloud conditions area and flood are the major challenges for region analysis. The proposed works predict the weather based on the attributes such as temperature, wind, and humidity using machine learning models. Computer-Aided prediction systems using machine learning models have successfully been used in the weather prediction domain. Hence, in this study, the weather data is classified and predicted effectively based on evaluating the performance of the models. The classification is analyzed using Classification and Regression Trees (CART), K-nearest neighbor, Random Forest, and Support Vector Machine. The performance of the proposed model achieved the highest classification accuracy of around 89% for the Random Forest classifier.