Abstract

Climate change is a threat that is being faced by us and it is a reality that this is a man-made phenomenon. Studies have shown that human activities after the 1850s have increased emission of carbon gases into the atmosphere leading to increased global atmospheric temperatures and this impacting sustainable development on a large scale. A solution to this disaster should also be provided by us and it lies in creating policies and practices that will lead to reduced carbon emissions and atmospheric temperature. Artificial intelligence techniques can analyse big data that will help build models to predict changes in the climatic patterns. This study uses traditional, machine learning and deep learning models to predict temperature based on a data set from the year 1850 to 2013. The findings suggest that XG boost and LightGBM are better than other models in terms of prediction accuracy. This indicates that AI models are critical in terms of predicting climate change patterns and in proactive monitoring and adaptation to environmental issues that affect sustainable development.