Abstract: Rainfall prediction is a crucial aspect of meteorology and environmental planning. This abstract presents an overview of the subject, highlighting its significance and methods. Rainfall prediction involves the use of various meteorological data sources, including historical weather records, satellite imagery, and atmospheric parameters, to forecast when, where, and how much precipitation will occur in a specific region. Machine learning models and statistical techniques play a vital role in this process, enabling the development of accurate predictive models. These models consider factors such as temperature, humidity, wind patterns, and geographical features. Accurate rainfall prediction is essential for agricultural planning, disaster management, and water resource allocation. Additionally, it aids in mitigating the impact of extreme weather events and climate change. Advances in technology and data collection methods continue to enhance the precision and reliability of rainfall predictions, contributing to more informed decision-making and the overall resilience of societies and ecosystems.

Keywords: Rainfall, Rainfall prediction, Geographical feature, Classification, Random forest, Decision tree, Machine Learning

Prof. Swati Dronkar, Vaishali Bisne, Shrutika Kumbhare, Vaidehi Gotmare,Shikhar Pathak, Shreyash Rahangdale, "Rainfall Prediction", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13117.

PDF | DOI: 10.17148/IJARCCE.2024.13117

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