Comparative Analysis of Machine Learning Algorithms for Geothermal Gradient Prediction in Columbia

Abstract In geologically diverse regions like Colombia, the geothermal gradient plays a critical role in energy exploration and resource management. It directly impacts the definition of subsurface temperature profiles, the characterization of geothermal reservoirs, and the economic feasibility of energy extraction projects. Accurate prediction of geothermal gradients is essential for sustainable and efficient energy development, making it a high priority for both traditional and renewable energy industries. Previous research has shown the potential of machine learning (ML) techniques, such as Extreme Gradient Boosting (XGBoost) for this task, but not much have explored the comparative performance of multiple ML algorithms. The aim of this study is to evaluate and compare multiple ML algorithms, such as Gradient Boosting Regressor (GBR), Random Forest (RF), K-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP), for predicting geothermal gradients using a dataset obtained from well log measurements across Colombia. Models were compared based on performance metrics such as R-squared (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) to identify the most accurate and robust algorithm. The RF model achieved the best test performance (R2=0.54), showing robust generalization. While XGBoost showed strong test results (R2=0.51), it exhibited signs of overfitting compared to its exceptional training score (R2=0.93). KNN delivered moderate accuracy as well (R2= 0.53), while MLP underperformed (R2=0.43). The results position RF as the most reliable algorithm for this application, providing valuable insights to optimize geothermal exploration and support Colombia's renewable energy transition through data-driven approaches.