Machine Learning Techniques for Weather Prediction: A Review

Abstract

Weather prediction and forecasting is considered as one of the technologies/processes with high priority and which always remains as a touch task due to its complexity. Various sectors of the economy are influenced directly or indirectly by weather. Also, major disasters caused by weather conditions like flood, cyclone, storm, drought etc. have negative impact on the society. Weather prediction is a crucial meteorological application. Prediction and Forecast systems of various weather derivatives will help people plan and take informed decisions. For instance, prediction of weather derivative such as rainfall plays a vital role in agriculture industry for planning suitable crop cultivation and water resource management. Weather derivatives can be predicted using meteorological data as well as using satellite images. Machine Learning techniques (MLT) are being used in various applications and are also extensively used for weather prediction. This study focuses on exploring different MLT used by researchers to predict weather conditions. MLT are effective in prediction and forecasting of weather variables with good accuracy. MLT coupled with Pre-processing techniques, hybrid MLT and deep learning models further enhance the accuracy of the models.