Bibliometric Study : Rainfall Classification - Prediction using Machine Learning Methods

Abstract

This Research aims to review the machine learning methods used for classifying or predicting rainfall, using various features from existing data. The study used a bibliometric approach to search for metadata related to rainfall classification and prediction studies using machine learning keywords in Scopus journals. There found 94 metadata articles stored in a Comma Separated Values (CSV) file. The data in this article used published articles from 2014 to 2023 with relevant topics. The study provides information on the latest machine learning methods used for classifying or predicting rainfall. The findings of the study include an increase in published articles by 221.43% from 2018 to 2022. The article titled "An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives" by Cramer S., Kampouridis M, Freitas A.A, and Alexandridis A.K received the highest citation count of 129. The study also classified 15 keywords into 3 clusters, with common and fewer keywords. India emerged as the country with the most publications on classifying or predictiong rainfall, and the subject areas of computer science and engineering dominated the distribution of articles. Developing the use of deep learning methods and adding feature extraction algorithms in selecting features used to model data can improve the efficiency and accuracy of the rainfall classification - prediction process. The development of research data using radar images with the type of image processing research can also be maximised for research related to classification - prediction of rainfall using machine learning methods.

Keywords

• bibliometric
• rainfall
• classification
• prediction
• machine learning