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, mean absolute percentage error (MAPE), and variance accounted for (VAF) to elucidate their predictive accuracy and generalization potential in the lower Friulian Plain (north-eastern Italy) where a geothermal reservoir is present. Among the algorithms scrutinized, XGBoost emerges as a predictive exemplar, achieving a remarkable R2 score of 0.9930 on the test dataset, with consistently low RMSE of 0.788, MAE of 0.587, MAPE of 1.909, and high VAF of 99.30, reaffirming its exceptional precision and robustness. It is worth noting that the other four models show slightly weaker performance than XGBoost, while Elastic Net shows moderate predictive power, which illustrates the complexity of the database. The Wilcoxon signed-rank test confirmed the superior performance of XGBoost in estimating geothermal temperatures compared to other algorithms, with statistical evidence supporting its precision and reliability. A Monte Carlo simulation for uncertainty analysis underlined the importance of model selection, accuracy and uncertainty management in the planning of geothermal projects in the lower Friulian Plain. A sensitivity analysis was performed to identify the main factors influencing the temperature prediction. Among the parameters considered, hydrogen carbonate the highest significance at 0.51, which is essential for accurate temperature prediction because of its buffering capacity which directly influences water's thermal properties. Magnesium and electrical conductivity each contribute with 0.11, also play significant roles due to their impact on the water's heat retention and distribution capabilities. Water depth, with a value of 0.08, also has a significant influence on the temperature profiles in prediction models. In summary, the accurate prediction of XGBoost for the temperature of aquifer in carbonate reservoirs in the lower Friulian Plain, underline its value for optimizing geothermal resources and highlight most important influences on temperature.