文章基本信息

标题：Ensemble Learning for Rainfall Prediction
其他标题：Ensemble Learning for Rainfall Prediction
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作者：Nor Samsiah Sani ; Abdul Hadi Abd Rahman ; Afzan Adam 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：11
DOI：10.14569/IJACSA.2020.0111120
出版社：Science and Information Society (SAI)
摘要：Climate change research is a discipline that analyses the varying weather patterns for a particular period of time. Rainfall forecasting is the task of predicting particular future rainfall amount based on the measured information from the past, including wind, humidity, temperature, and so on. Rainfall forecasting has recently been the subject of several machine learning (ML) techniques with differing degrees of both short-term and also long-term prediction performance. Although several ML methods have been suggested to improve rainfall forecasting, the task of appropriate selection of technique for specific rainfall durations is still not clearly defined. Therefore, this study proposes an ensemble learning to uplift the effectiveness of rainfall prediction. Ensemble learning as an approach that combines multiple ML multiple rainfall prediction classifiers, which include Naïve Bayes, Decision Tree, Support Vector Machine, Random Forest and Neural Network based on Malaysian data. More specifically, this study explores three algebraic combiners: average probability, maximum probability, and majority voting. An analysis of our results shows that the fused ML classifiers based on majority voting are particularly effective in boosting the performance of rainfall prediction compared to individual classification.
关键词：Ensemble learning; classification; rainfall prediction; machine learning