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Top1. Introduction
Weather forecasting is a mechanism to predict weather i.e. changes in the atmospheric conditions for a given location. It is essential to predict weather as it has a significant impact on both living and non-living things. Forecasting weather is a crucial one as the changes in weather has a severe impact on the life of people, agricultural field (Kaur, S., & Cheema, S. S. 2017). Changes in weather are due to the molecules such as oxygen, carbon dioxide, Nitrogen dioxide and sulfur dioxide (Kaur, S., & Cheema, S. S. 2017). Weather forecasting can protect the people from disasters like Tsunami, heavy rainfall and also protects the crops thereby helping the farmers. The forecasting of weather is important in many fields such as climate monitoring, detecting drought, production of agriculture crops, pollution control etc. Forecasting weather is still a major issue in the department of metrology. Though, the technology grows in advance, the accurate prediction of weather is still a crucial question. Several researchers are concentrating on forecasting weather by building a model using machine learning algorithms. Now a day’s lot of IoT devices is used to sense the changes in parameters such as temperature, wind, humidity, level of oxygen etc. Thus using these measurements, a model can be built using machine learning algorithms for accurate prediction of weather. Numerical Weather prediction is also widely used to predict the weather.
There are two methods used to predict weather viz. Empirical method and Dynamic method. Empirical method is the one which relies on past historical data to build the model to predict the future. The machine learning algorithms such as decision tree, Linear regression etc. fall into empirical methods. Dynamic methods are the one where the expectations from the physical model are used to predict the future (Bhatkande, S. S., & Hubballi, R. G. 2016). The dynamic approach is otherwise termed to be computer modeling and it is suitable for predicting large scale weather condition and does not forecast short term weather condition (Devi, C. J., Reddy, B. S. P., Kumar, K. V., Reddy, B. M., & Nayak, N. R. 2012).