Traditional GIS-based statistical models are intended to extrapolate patterns of settlements and their interactions with the environment. They contribute significantly to our knowledge of past human–land relationships. Yet, these models are often criticized for their empiricism, lopsided specific factors, and for overlooking the synergy between variables. Though largely untested, machine learning and artificial intelligence methods have the potential to overcome these shortcomings comprehensively and objectively. The northeastern Tibetan Plateau (NETP) is characterized by diverse environments and significant changes to the social system from the Neolithic to Bronze Age. In this study, this area serves as a representative case for assessing the complex relationships between settlement locations and geographic environments, taking full advantages of these new models. We have explored a novel modeling case by employing GIS and random forests to consider multiple factors, including terrain, vegetation, soil, climate, hydrology, and land suitability, to construct classification models identifying environmental variation across different cultural periods. The model exhibited strong performance and a high archaeological prediction value. Potential living maps were generated for each cultural stage, revealing distinct environmental selection strategies from the Neolithic to Bronze Age. The key environmental parameters of elevation, climate, soil erosion, and cultivated land suitability were calculated with high weights, influencing human environmental decisions synergistically. Furthermore, we conducted a quantitative analysis of temporal dynamics in climate and subsistence to understand driving mechanisms behind environmental strategies. These findings suggest that past human environmental strategies were based on the comprehensive consideration of various factors, coupled with their social economic scenario. Such subsistence-oriented activities supported human beings in overcoming elevation limitation, and thus allowed them to inhabit wider pastoral areas. This study showcases the potential of machine learning in predicting archaeological probabilities and in interpreting the environmental influence on settlement patterns.

Groundwater seepage plays a critical role in the long-term safety of high-level radioactive waste (HLW) disposal, yet its characterization remains challenging due to the complexity of fractured rock media. This study introduces the Double-Layered Seismo-Electric Method (DSEM) for imaging groundwater seepage fields with enhanced sensitivity and spatial resolution. By integrating elastic wave propagation with electrokinetic coupling in a stratified framework, DSEM improves the detection of hydraulic gradients and preferential flow pathways. Application at a representative disposal site demonstrates that the method effectively delineates seepage channels and estimates hydraulic conductivity, providing reliable input parameters for groundwater flow modeling. These results highlight the potential of DSEM as a non-invasive geophysical technique to support safety assessments and long-term monitoring in deep geological disposal of high-level radioactive waste.

The long-term safety of high-level radioactive waste (HLW) disposal depends on understanding groundwater migration in complex, low-permeability rock environments. In particular, fractured rock formations may serve as potential pathways for radionuclide transport over geological timescales. This study investigates groundwater flow at the Beishan site in Gansu Province, a candidate HLW repository, using a three-dimensional sonar vector seepage field detection system. Applied to the Xinchang and Shiyuejing fault zones, the method measured seepage velocities of approximately 1×10-10 m/s, with dominant flow directions from northwest to southeast and northeast to southwest, respectively. Results show that variations in permeability closely follow regional structural fracture patterns, and the findings align with existing hydrogeological data indicating average rock mass permeability near 1×10-8 m/s. The detection system achieved a velocity resolution of 1×10-10 m/s and directional accuracy within 2°, confirming its precision and reliability. This work demonstrates a practical and accurate approach for identifying groundwater flow in hard, nearly impermeable rock masses, offering critical support for HLW repository evaluation and broader geotechnical applications such as dam monitoring, underground oil storage, and dewatering of deep excavations.

The southwestern region of China is highly susceptible to landslides due to steep terrain, fractured geology, and intense rainfall. This study focuses on the Xiluodu Reservoir area in Yunnan Province and applies Geographic Information System (GIS) techniques together with ten key spatial factors—such as slope, lithology, elevation, and distance to rivers—to perform a quantitative landslide risk assessment. In addition to the individual Certainty Factor (CF) and Logistic Regression (LR) models, we developed an integrated CF–LR coupled model to overcome their respective limitations: the CF model’s sensitivity to specific factor attributes but neglect of factor interactions, and the LR model’s robust weight estimation but weak representation of attribute heterogeneity. By combining these strengths, the CF–LR model achieved superior predictive performance (AUC = 0.804), successfully capturing 92.5% of historical landslide events within moderate-to-high risk zones. The results show that lithology, slope angle, and proximity to rivers and roads are dominant controls on susceptibility, with landslides concentrated on soft rock slopes of 30–40° and within 600–900 m of rivers. Compared with previous coupled approaches in similar mountainous reservoir settings, our CF–LR model provides a more balanced and interpretable framework, enhancing both classification accuracy and practical applicability. These findings demonstrate that GIS-based CF–LR integration is a novel and reliable tool for landslide susceptibility mapping, offering important technical support for disaster prevention and risk management in large reservoir regions.

Rapid urbanization and complex topography complicate Urban Heat Island (UHI) spatio-temporal dynamics. Traditional models and coarse-resolution imagery often fail to capture fine-scale, spatially non-stationary seasonal driving mechanisms. This study investigates the multi-dimensional drivers of surface thermal dynamics in Kunming, a typical low-latitude plateau city, using seasonal median LST composite (2018–2025). Integrating eXtreme Gradient Boosting (XGBoost) with eXplainable Artificial Intelligence (XAI) models decoupled the nonlinear impacts of these drivers. Results reveal a seasonal thermal dichotomy: Summer exhibits the most intense UHI effect with extreme peak temperatures, while Spring presents an anomaly where natural and vegetated Local Climate Zones (LCZs) show pronounced warming. SHapley Additive exPlanations (SHAP) analysis identified a seasonal rotation: anthropogenic and structural factors dominate Summer and Autumn warming, whereas natural and topographic regulators govern Spring and Winter. GeoShapley deconstruction demonstrated strong spatial non-stationarity. Building-density warming is amplified in poorly ventilated urban cores, and fragmented vegetation’s cooling is offset by anthropogenic heat during peak summer. This study provides new insights into the seasonal drivers of urban thermal environments in plateau cities.

In the context of China’s dual-carbon goals of carbon peaking and carbon neutrality, urban low-carbon planning has become central to achieving sustainable urban transformation. However, its implementation remains hindered by fragmented legal frameworks, weak regulatory coordination, limited incentive mechanisms, and insufficient public participation. This study examines how institutional safeguard mechanisms can enhance the effectiveness and enforceability of urban low-carbon planning. Using a comparative case analysis of representative Chinese cities, it investigates the current institutional arrangements, identifies key governance deficiencies, and explores feasible pathways for systemic improvement. The findings reveal that legal institutionalization, integrated regulatory frameworks, diversified incentive policies, and participatory co-governance are critical to advancing low-carbon planning and ensuring policy coherence. These insights can improve administrative efficiency, enhance stakeholder engagement, and create a resilient governance structure that supports long-term carbon reduction. The study contributes to the theoretical understanding of urban low-carbon governance and provides actionable policy guidance for cities in developing countries pursuing sustainable and inclusive low-carbon transitions under the global climate governance framework.