A Wide Scale Survey on Weather Prediction Using Machine Learning Techniques.

Several losses had been witnessed due to many natural calamities like earth quakes, storms, cyclones, etc. These natural calamities have direct or indirect effects on the lives of billions of people across the world. The prediction of environmental impact due to the changes in weather had been a critically challenging task. In countries like India, where agriculture is the livelihood of many people (49.5%) and rainfall is very essential for the cultivation of crops, rainfall is very much needed to all forms of lives. Extreme rainfall has its effects on the economy of any country. Heavy loss of lives and properties had been encountered due to havoc of flood in varying degrees. In this research work, the rainfall forecasting is highly focussed and it discusses on several models of weather prediction. Note that in the previous decades, many researchers have made some serious attempts to reach out with forecasting systems for weather prediction (which include statistical and analytical models for rainfall prediction) but maximum models proposed by the researchers are found to be unfit in terms of less accuracy, when these proposed prediction models are applied on a large scale. The research work presents the reviews of works that are proposed by many pioneers, who had taken lots of efforts arrive at a good prediction system. In this work, it is also found that that there had been a big gap between the prediction reports/weather news and the actually happening. This paper considers most of the features belonging to the models found from scientific articles published across the globe to find the factors that are widening the gap between the forecast data and the actual phenomenon. [ABSTRACT FROM AUTHOR]