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DOI: 10.14569/IJACSA.2020.0111120
PDF

Ensemble Learning for Rainfall Prediction

Author 1: Nor Samsiah Sani
Author 2: Abdul Hadi Abd Rahman
Author 3: Afzan Adam
Author 4: Israa Shlash
Author 5: Mohd Aliff

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 11, 2020.

  • Abstract and Keywords
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Abstract: 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.

Keywords: Ensemble learning; classification; rainfall prediction; machine learning

Nor Samsiah Sani, Abdul Hadi Abd Rahman, Afzan Adam, Israa Shlash and Mohd Aliff, “Ensemble Learning for Rainfall Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 11(11), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111120

@article{Sani2020,
title = {Ensemble Learning for Rainfall Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111120},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111120},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {11},
author = {Nor Samsiah Sani and Abdul Hadi Abd Rahman and Afzan Adam and Israa Shlash and Mohd Aliff}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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