Machine Learning Algorithms as State-of-the-Art Tools for Prediction of Climatic Conditions: With Focus on Global Land Temperatures

Temperatures in various places are drastically increasing or reducing. Skyrocketing land temperatures are expected to change the frequency and intensity of current land temperature extremes. Determining the evolving trends in land temperatures is thus immeasurable. Most importantly, global land temperatures can be forecasted using machine learning algorithms. In our study, polynomial regression and artificial neural networks were used to predict global land temperatures for the next 100 years. Scenario analysis was also done using business-as-usual, moderate mitigation, and aggressive mitigation approaches. All data visualizations of the historical data, predicted data, and data from scenario analysis were done with the aid of MATLAB R2024a. Predictions from polynomial regression revealed that a rapid increase in global land temperatures was to occur from 2012 to 2032 while a rapid increase in global land temperatures was predicted to occur from 2012 to 2032 followed by a gentle rise from 2032 to 2100 based on the artificial neural networks’ prediction. The results of the scenario analysis revealed a dire need for aggressive mitigation to be adopted and implemented as soon as possible. Despite the predictions made by the two algorithms, predictions by artificial neural networks were more reliable compared to those obtained from polynomial regression.