Machine Learning Applications in Predicting Climate Change Patterns

Abstract

The increasing influence of the climate change and its associated impacts has further given rise to the development of advanced computational tools to understand, predict and mitigate the environmental risks. As a subfield of Artificial Intelligence (AI), Machine Learning (ML) offers powerful methods for the analysis of complex climate data, pattern recognition and the prediction of changes in the environment. This paper is focused on the application of ML in forecasting climate change patterns with special emphasis on the application of ML in forecasting the patterns of change in temperature, extreme weather, rise in sea-level, and emission of greenhouse gases. Incorporating the use of supervised, unsupervised and reinforcement learning, researchers are in a position to be able to reveal the underlying trends, increase decision making predictability and provide better climate mitigation and adaptation strategies. More importantly, a combination of remote sensing data, climate models, and ML algorithms will help create dynamic forecasting systems, which can be functional at global, regional, and local levels. Other challenges discussed by the paper include the heterogeneity of data, the explainability of the model, and the limitations of computing which are also recommended as new areas of future research. It is found that ML is not only complementary to the conventional climate modeling, but it yields revolutionary information on proactive environmental planning and sustainable policy formulation.