Artificial intelligence applications for accurate geothermal temperature prediction in the lower Friulian Plain (north-eastern Italy)

Geothermal energy as a sustainable and clean energy source depends on the accurate estimation of reservoir temperatures. Understanding aquifer temperatures is crucial for optimizing low-enthalpy geothermal system exploitation. Advances in predictive algorithms can improve geothermal efficiency, while conventional methods of indirect temperature measurement and assumptions in geochemical analysis lead to uncertainties. As a solution, this study presents a comprehensive evaluation of six machine learning algorithms including eXtreme gradient boosting (XGBoost), decision tree, generalized regression neural network, extreme randomized trees, radial basis function, and elastic net. We employed essential performance metrics including coefficient of determination (R2) score, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and variance accounted for (VAF) to elucidate their predictive accuracy and generalization potential in the lower Friulian P...