Machine Learning and Spatio-Temporal Patterns in Climate Change

Abstract

Climate change is one of our planet's most significant concerns, affecting ecosystems, weather patterns, and human populations all around the globe. Understanding the complex spatiotemporal pattern associated with climate change is critical for making sound judgments and developing effective mitigation solutions. In recent years, the analysis of large and complex climate data sets has been revolutionized with the help of machine learning methods. This study uses datasets from the Kaggle website and for data preparation the Min-Max method is used to ensure the data which is suitably scaled. Following that, the decision tree machine learning technique is used for feature extraction, allowing the identification of relevant climate-related elements. Finally, the study employs spatiotemporal analysis to create prediction models for predicting climate change trends. This interdisciplinary approach emphasizes the important connection between machine learning and climate science, giving vital insights for climate researchers and policymakers dealing with the issues presented by climate change. The suggested approaches greatly outperform current existing models, obtaining an astounding accuracy of 97.99%. This collaboration allows for the creation of precise prediction models, which help in climate forecasting, early warning systems, and climate impact assessments.