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Geothermal resources are increasingly gaining attention as a competitive, clean energy source to address the energy crisis and mitigate climate change. The Wugongshan area, situated in the southeast coast geothermal belt of China, is a typical geothermal anomaly and contains abundant medium‐ and low‐temperature geothermal resources. This study employed hydrogeochemical and isotopic techniques to explore the cyclic evolution of geothermal water in the western Wugongshan region, encompassing the recharge origin, water–rock interaction mechanisms, and residence time. The results show that the geothermal water in the western region of Wugongshan is weakly alkaline, with low enthalpy and mineralization levels. The hydrochemistry of geothermal waters is dominated by Na‐HCO3 and Na‐SO4, while the hydrochemistry types of cold springs are all Na‐HCO3. The hydrochemistry types of surface waters and rain waters are Na‐HCO3 or Ca‐HCO3. The δD and δ18O values reveal that the geothermal waters are recharged by atmospheric precipitation at an altitude between 550.0 and 1218.6 m. Molar ratios of major solutes and isotopic compositions of 87Sr/86Sr underscore the significant role of silicate weathering, dissolution, and cation exchange in controlling geothermal water chemistry. Additionally, geothermal waters experienced varying degrees of mixing with cold water during their ascent. The δ13C values suggest that the primary sources of carbon in the geothermal waters were biogenic and organic. The δ34S value suggests that the sulfates in geothermal water originate from sulfide minerals in the surrounding rock. Age dating using 3H and 14C isotopes suggests that geothermal waters have a residence time exceeding 1 kaBP and undergo a long‐distance cycling process. [ABSTRACT FROM AUTHOR]

Effective landslide hazard prevention requires accurate landslide prediction models, and the data-driven approaches based on deep learning models are gradually becoming a hot research topic. When training deep learning models, it is always preferable to have a large dataset, while most available landslide monitoring data are limited. For data missing or data sparseness problems, conventional interpolation methods based on mathematical knowledge lack mechanism interpretability. This paper proposes that numerical simulations can be used to expand the deep learning dataset we need. Taking the Jiuxianping landslide in the Three Gorges Reservoir Area (TGRA) as the geological background, a finite element numerical model was established, and the landslide displacement time series data were solved considering the boundary conditions of reservoir water level change and precipitation. Next, based on three metrics: Euclidean distance, cosine similarity, and dynamic time warping (DTW) distance, the time series similarity between the displacement data obtained from simulation and data obtained from actual monitoring were verified. Finally, the combined deep learning model was built to predict the displacement of the Jiuxianping landslide. The model was trained on both the simulated and monitored datasets and tested by the last 12 monitored data points. Prediction results with the testing set showed that the models trained using the expanded training set from numerical simulations exhibited lower prediction errors, and the errors had a more concentrated distribution. The results suggest that this landslide displacement prediction method combining numerical simulation and deep learning can solve the problem of inadequate datasets due to low monitoring frequency, as well as provide an interpretation of the physical mechanism for data vacancy filling. [ABSTRACT FROM AUTHOR]