Model Development for Geothermal Temperature Estimation Using Group Method of Data Handling Based on Discrete Differential Evolution
Grant Charles Mwakipunda, Ibrahim Al-Wesabi, Maimuna Iddy Abdy, Mbega Ramadhani Ngata, Wahib Ali Yahya, Long Yu · SPE Journal · 2025
Summary This study introduces the group method of data handling with discrete differential evolution (GMDH-DDE) for estimating geothermal temperature and compares its performance with the generalized group method of data handling (GS-GMDH) and GMDH algorithms. GMDH-DDE outperforms accuracy, efficiency, and computational cost, achieving coefficient of determination (R2) values of 0.9999 and 0.9992 for training and testing, respectively. It also demonstrated minimal errors with root mean square error (RMSE) and mean absolute error (MAE) values of 0.0099 and 0.0032 during training and 0.1571 and 0.0099 during testing. Additionally, GMDH-DDE’s training time of 3.12 seconds highlights its computational efficiency, making it suitable for real-time applications and large datasets. Accurate geothermal temperature estimation is crucial for optimizing geothermal energy applications, such as drilling, power plant operations, and geothermal heat pump systems. In conclusion, GMDH-DDE offers a highly accurate, efficient, and computationally feasible method for geothermal temperature estimation, benefiting both research and practical applications in geothermal energy. The modified GMDH-DDE, as an enhanced version of GMDH, can serve and be adapted as a more effective alternative for geothermal temperature estimation across diverse geological settings worldwide.