Enhanced regression method for weather forecasting

Weather prediction is gaining popularity very rapidly in the current era of artificial intelligence and Technologies. It is essential to predict the temperature of the weather for some time. Traditionally, weather predictions are performed with the help of large complex models of physics, which utilize different atmospheric conditions over a long period of time. These conditions are often unstable because of perturbations of the weather system, causing the models to provide inaccurate forecasts. The models are generally run on hundreds of nodes in a large high-performance computing (HPC) environment, which consumes a large amount of energy. In this paper, LightGBM Regression parameters are tuned by using an optimization technique. Differential evolution (DE) is used to optimize the LightGBM regressor for estimating and forecasting the weather in the fore coming days.