Machine and deep learning-based prediction of potential geothermal areas in Hangjiahu Plain by integrating remote sensing data and GIS

Geothermal resources are renewable and clean energy sources with diverse applications. However, it only accounted for less than 1 % of all thermal energy use worldwide in 2021. Successfully locating and drilling geothermal wells is challenging. Our study aimed to establish an effective model to evaluate the geothermal potential in the Hangjiahu Plain of China. We incorporated lithology, distances to faults, surface water system, seismic points, magmatic and volcanic rocks, and remote sensing data, including land surface temperature, normalized difference vegetation index, and slope into the geothermal potential evaluation system and for the first time, compared the performance of three machine and one deep learning models: support vector machine, adaptive boosting, light gradient boosting machine (LightGBM) and TabNet for predicting the geothermal potential in the Hangjiahu Plain. Results showed that TabNet had the best performance, achieving an accuracy of 95.95 %. LightGBM closely followed TabNet, with an accuracy of 91.89 % but exhibited ten times higher efficiency than TabNet in training complex geothermic data. Compared to other models, the map of areas having high geothermal potential generated by TabNet fits the distribution of geothermal drilling points with high outlet water temperatures, which further indicates that TabNet has good predictive performance.