Abstract
In weather forecasting, we predict atmospheric conditions for specific location and time. Meteorology is used to plot atmospheric change for the desire location by collecting cumulating data. Data can be processed thoroughly in the backend to obtain accurate weather predictions. Data science plays a major role in the quantitative data processing. Many streams, organizations and business rely on the accuracy of weather forecasting; similarly, the current situation of weather is equally important for individuals. The decision of plantation, irrigation and harvesting in agriculture, air traffic control, construction work and many other occupations depends on the climatic conditions. Forecasting of weather is all about the collection of data, processing of data and analysis of data. For previous data reproduction and estimation, weather models are essentially required. Therefore, it is important to have the correct piece of information, and which is to be near to exact decisions. However, over the most recent decade, machine learning (ML) has introduced and implemented in atmospheric science. With the support of the relative predictors and accessible data, weather information is calculated by ML. From consolidated results for ML and predictive modeling, we can get more accurate data results on how ML helps to improve the physically grounded models. ML and sophisticated models are utilized by the physical models and estimated information on big computer systems to forecast the weather. With the help of a Python application programming interface (API), we can improve the creation and collection of information and peruse meteorological information that has been created, and by using TensorFlow, we can create artificial neural networks models have been created. ML gives unsupervised and supervised learning techniques to forecast weather with an insignificant mistake. In this chapter, we focused on how to generate more accurate and correct weather forecasting for enormous timeframes. This chapter contains the use of regression decision tree (DT), linear regression, and clustering techniques, DT classification, binary logistic regression and principal component analysis of ML to forecast weather achieving higher accuracy.
