A comparative study on data mining models for weather forecasting: A case study on Chittagong, Bangladesh

Abstract

The primary focus of this study is to analyze and predict the patterns of this essential feature of the natural world. This study analyses and predicts the daily weather patterns of a specific urban area. This article utilizes weather data over 20 years to analyze the climate patterns of Chittagong city. A total of 12 distinct Data Mining models were employed to predict daily weather patterns. The algorithms can be categorized into three distinct types, namely rules-based, tree-based, and function-based. To evaluate the effectiveness of the models, various performance metrics were computed, including precision, recall, accuracy, F-measure, and the area under the receiver operating characteristic curve (ROC area). Based on the results obtained, it can be concluded that among the 12 algorithms evaluated, J48 exhibits the highest level of performance and accuracy. The J48 classifier demonstrated an accuracy of 82.30%, precision of 82.40%, recall of 82.20%, f-measure of 84.20%, and a ROC area of 97.8%. Furthermore, a comprehensive analysis of the confusion matrix for all twelve algorithms was conducted to facilitate further evaluation.

Keywords

• Weather prediction
• Temperature
• Data mining
• Weather forecasting