In this chapter, we will discuss the application of Python using the polynomial regression approach for weather forecasting. We will also evoke the role of Pearson correlation in modifying the trend of climate forecast. The weather data were processed via Aqua Crop by introducing daily climate observations. Accordingly, the software outputs are: reference evapotranspiration, maximum and minimum temperature, and precipitation. Additionally, we focused on the interference of the input data on the efficiency of predicting climate change scenarios. For that matter, we used this machine learning algorithm for two case studies, depending on the type of input data. As a result, we found that the outcome of polynomial regression was very sensitive to those input factors.
Part of the book: Recent Advances in Polynomials