(untitled) (International Journal of Statistical Sciences, 2023)
Around the world, forecasting rainfall has been regarded as one of the most difficult task. Exact and timely rainfall forecasting may be extremely helpful. By uncovering novel links between the readily available elements of historical data, data mining algorithms may accurately anticipate the amount of rainfall. Therefore, it remains intriguing to forecast rainfall data with both the highest degree of accuracy by combining and improving various data mining approaches in case of different weather stations. In this study we compare the forecasting performance of different data mining techniques such as Classification and Regression Trees (CART), Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Random Forest (RF), and Linear Discriminant Analysis (LDA) in case of Bogura and Rangpur district of Bangladesh. For this analysis, the monthly time series data from January 1964 to December 2017 are taken into account. For empirical investigations, the data mining process, including data collection, data pre-processing, modeling, and assessment, is closely adhered to. The empirical study shows that SVM approach is the best option for predicting rainfall in the case of both Bogura and Rangpur district, Bangladesh, for the next time period. The above study will be useful in providing information to support crop, water, and flood control, which will protect people’s lives and property and promote economic in its growth. International Journal of Statistical Sciences , Vol. 23(2), November, 2023, pp 47-62