Enhancing Weather Prediction Accuracy with CNN-Based Machine Learning

The accurate as well as efficient prediction of the weathers is considered a big scientific and technological challenge due to the highly dynamic and complex nature of the atmosphere under which such severe developments occur. This project thus focuses on the utilization of datamining techniques, mostly clustering and classification, to improve the accuracy of prediction-enabled weather forecasting systems. Historical and real-time weather data, including satellite images, radar scans, etc., are utilized to extract patterns and relationships, which lend towards predictive modeling. A Multilayer Perceptron (MLP) is used for mapping temperature, humidity, and rainfall as different weather variables' classes. The pre-processing of raw data into useful and relevant features plays a very important role in improving model performance. In this context, effective data mining and machine learning methods put forward by this study will contribute toward more reliable weather prediction against the universe of concern regarding accuracy and efficiency on meteorological systems.