Rainfall Prediction using Machine Learning
In this paper contributes by providing critical analysis and review of the latest data mining techniques, used for rainfall prediction. Published papers from year 2013 to 2017 from renowned online search libraries are considered for this research. This review will serve the researchers to analyse the latest work on rainfall prediction with the focus on data mining techniques and also will provide a baseline for future directions and comparisons.
Accurate rainfall prediction is more complex today due to climate variations. Researchers consistently have been working to predict rainfall with maximum accuracy by optimizing and integrating data mining techniques. Data mining algorithms are classified as supervised and unsupervised.
- Rainfall is a complex atmospheric process, which depends upon many weather related features.
- Accurate and timely rainfall prediction can be helpful in many ways such as planning the water resources management, issuance of early flood warnings, managing the flight operations and limiting the transport & construction activities.
In this research a list of significant research questions was identified and then a systematic research process was followed to extract and shortlist the most relevant research articles from renowned digital search libraries. Answers of the identified questions were explored by critically reviewing the shortlisted articles. The research focus on the domain of rainfall prediction has been increasing since last decade and so are the problem areas. So it was concluded that enhancements, optimizations and integrations of data mining methods are vital to explore and solve these problems.
Various models and techniques are available today for effective rainfall prediction but still there was a lack of a compact literature review and systematic mapping study which could reflect the current problems, proposed solutions and the latest trends in this domain.
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