Tunnels serve as vital arteries in the realm of transportation and infrastructure, facilitating the seamless flow of movement across challenging terrains. With the increasing demand experienced by the traffic network on the Tibetan Plateau, deep-buried, lengthy tunnels have become one of the extremely important types of roads for local residents to pass through. Geological disaster susceptibility mapping by hybrid models has been proven to be an effective means to reduce the losses caused by disasters in a large area. However, there has been relatively little research conducted in tunnel areas with significant human activity. To explore the feasibility of conducting geological disaster susceptibility assessment in tunnel areas, we chose the Atal Tunnel as a study project; as a strategic passageway, this exemplifies the significant geological hurdles encountered on the Tibetan Plateau. Employing multi-source remote sensing data, we meticulously mapped the distribution of geological disasters and identified nine environmental and geological variables pivotal for susceptibility evaluation. We harnessed interpretable ensemble learning models to assess this susceptibility, comparing the efficacy of four distinct models: the weight of evidence method (WoE), the frequency ratio (FR), logistic regression (LR) and the support vector machine (SVM). The precision of our findings was rigorously tested using metrics such as the percentage of disaster area encompassed within each risk level, the Area Under the Curve (AUC) value, and the Receiver Operating Characteristic (ROC) curve, and all results were highly accurate. Notably, the WoE-LR model achieved superior performance, boasting an impressive accuracy rate of 90.7%. Through model interpretation, we discerned that the alignment of the road line is the most critical determinant in the evaluation of tunnel geological disaster susceptibility, underscoring the high precision of our model. The extension and successful application of this research in plateau areas hold profound implications for sustainable development. Moreover, the practical application of these research findings will provide a practical reference for the design and construction of projects in similar plateau areas, with positive outcomes that extend well beyond the immediate geographical area of the projects.

The evaluation of landslide susceptibility plays a crucial role in preventing the risks associated with landslides and debris flows, providing valuable insights for the effective prevention and mitigation of geological hazards. However, there is limited research on high-altitude areas. Therefore, this study chose the western Tibetan Plateau as the study area, a representative area known for its susceptibility to landslides and high attitudes. In this study, seven factors were identified based on research objectives. Information value (IVM), weight of evidence (WOE), information value logistic regression (IVM-LR), weight of evidence logistic regression (WOE-LR), information value multi-layer perceptron (IVM-MLP) and weight of evidence multi-layer perceptron (WOE-MLP) were selected and compared for landslide susceptibility. The percentage of disaster area included in each risk level, the AUC value and the ROC curve were used to evaluate the accuracy of the results. The ROC curves of the results were close to the upper–left corner and the AUC values exceeded 0.85, an indication that all results were highly accurate. Moreover, the percentage of disaster area included for each risk showed an upward trend regarding susceptibility. The results indicated that the hybrid model exhibited superior performance in assessing landslide susceptibility at high altitudes. Overall, the results showed great significance regarding disaster prevention and mitigation measures of local governments.

Geothermal energy, with its promise of sustainability and a minimal environmental impact, offers a viable alternative to fossil fuels that can allow us to meet the increasing energy demands while mitigating concerns over climate change. Urban areas, with their large energy consumption, stand to benefit significantly from the integration of geothermal systems. With the growing need to harness renewable energy sources efficiently, the detection of urban subsurface resources represents a critical frontier in the pursuit of sustainability. The Guangdong Bay area, known for its abundant geothermal resources, stands at the forefront of this green energy revolution, so, in our study, we chose to evaluate Zhuhai City, which is a city representative of the resource-rich area of Guangdong. With the progress of geographic information system (GIS) technology, the land surface temperature (LST) has been used to monitor the spatial distribution characteristics of geothermal anomalies. However, relatively few studies have been conducted in the field of urban geothermal resources. In this study, we calculated the LST of Zhuhai City using Landsat 8 remote sensing data and then investigated the distributions of geothermal hot springs. Spatial data layers were constructed, including the geological structure, DEM and derivatives, lithology, and urban regions, and, based on technology with the integration of machine learning, their spatial correlations with geothermal anomalies were analyzed. The support vector machine (SVM) and the multilayer perceptron (MLP) were employed to produce maps of potential geothermal resources, and their susceptibility levels were divided into five classes: very low, low, moderate, high, and very high. Through model interpretation, we found the moderate-susceptibility class to dominate at 26.90% (SVM) and 46.27% (MLP) according to the two models. Considering the influence of artificial areas, we also corrected the original LST by identifying urban areas of thermal anomalies via the urban thermal anomaly leapfrog fusion extraction (UTALFE) method; following this augmentation, the results shifted to 24.16% (SVM) and 28.67% (MLP). Meanwhile, the area under the curve (AUC) values of all results were greater than 0.65, showing the superior performance and the high applicability of the chosen study area. This study demonstrates that data-driven models integrating thermal infrared remote sensing technology are a promising tool for the mapping of potential urban geothermal resources for further exploration. Moreover, after correction, the reclassified LST results of urban areas are more authentic and suitable for the mapping of potential geothermal resources. In the future, the method applied in this study may be considered in the exploration of more southeastern coastal cities in China.