Abstract : In general, weather forecasting is done with the use of enormously complicated physical science models that use a variety of environmental circumstances over a long period of time. Because of the annoyances of the climatic framework, these criteria are frequently fragile, causing the models to produce inaccurate forecasts. The models are mostly run on multiple hubs in a massive High-Performance Computing (HPC) environment that uses a lot of energy. In this research, we offer a climate expectation approach that uses historical data from various climate stations to create basic AI models that may provide meaningful forecasts for specific climatic conditions in the not-too-distant future within a given time frame. In this paper, we offer a climate expectation approach that uses historical data from several climate stations to create basic AI models that can anticipate certain climatic conditions in the not-too-distant future within a given time frame. Overall research performed into two stages; in first stage Principle component Analysis has been used to extract the irrelevant attributes from the datasets. In second stage five different machines learning algorithm used to predict temperature condition for midterm span & finally four performance indicators along with training time used to identify the best fitted model. From the result analysis it is seen that PCA based AdaBoost model is the fittest model with acquired the best outcome of R2, RMSE, MAE & MSE are 0.992, 0.539, 0.398 & 0.209 respectively. Beside of this present model also outperformed than the other state of art model proposed for midterm weather forecasting purpose.

Keyword : Weather Forecasting, PCA, Machine learning, Performance indicator