Abstract
Using scientific knowledge, weather forecasters can predict what the atmosphere will be like in a particular place. It predicts snow, cloud cover, rain, temperature and wind speed. Since weather predictions consist of multidimensional and nonlinear data, they are one of the world's most challenging problems. Various machine learning algorithms and methods have been used in data mining for weather prediction, including Support Vector Machines, supervised and unsupervised machine learning algorithms, artificial neural networks, FPGrowth Algorithms, Hadoop with Map Reduce, K-medoids, and Naive Bayes. This survey briefly explains the methods used to build weather forecasting models to assist researchers in choosing the appropriate method for their model.
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