Rainfall Prediction using Big Data Analytics: A Systematic Literature Review
Keywords:
Rainfall prediction, Big Data Analytics, SLRAbstract
With major ramifications for agriculture, water resource management, and disaster planning, rainfall prediction is an essential component of weather forecasting. The use of big data analytics techniques has become more commonplace in recent years as a means of improving rainfall prediction models' accuracy and dependability. The goal of this systematic literature review is to present a thorough summary of the state of the art in the field of big data analytics-based rainfall prediction research. The first section of this paper provides a thorough examination of the basic ideas and procedures used in rainfall prediction models. It emphasises how crucial it is to incorporate data from a variety of sources into predictive models, such as social media, meteorological, and remote sensing data. This complete overview of the state-of-the-art in big data analytics-based rainfall prediction is provided by this systematic literature review. It highlights the need for multidisciplinary collaboration between meteorologists, data scientists, and domain specialists to further develop the subject of rainfall prediction and its applications. It also identifies gaps in the current research and recommends prospective directions for future studies.
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