Predicting Glacier Ice Melt with Machine Learning to Address Climate Change

The accelerating decline in glacier mass due to climate change presents a significant threat to global water resources, sea levels, and ecosystem stability. This research integrates machine learning techniques to predict glacier ice melt patterns using historical mass balance data. Leveraging the publicly available global glacier mass balance dataset, the study investigates temporal trends and employs regression models— including Polynomial Regression Linear Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM)—to forecast future glacier behavior. Exploratory Data Analysis (EDA) reveals a strong negative correlation (−0.96) between year and cumulative mass balance, highlighting accelerated ice loss over recent decades. Among the models, Random Forest achieved the highest predictive accuracy (R² = 99.71%).(Working with a Two-Stage Ice Sheet Model, n.d.) followed by Decision Tree (R² = 99.57%), indicating their robustness in capturing nonlinear glacier dynamics. This machine learning framework serves as an effective tool for evaluating glacier degradation under varying emission scenarios and contributes valuable insights for environmental policy, climate impact assessment, and adaptation strategies.