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This study evaluates the effectiveness of various classification algorithms, including Logistic Model Tree, J48, Random Forest, and PART, in predicting daily precipitation occurrences using meteorological data from the Pars Abad station. The results indicate that the PART algorithm outperforms others with over 80% accuracy, particularly in the scenario utilizing 1-day lag input parameters. The findings suggest that employing these machine learning methods can significantly enhance the accuracy of daily prec

Author

santhakalyanmirdha

Language

EN