The high sensitivity of loess slopes to water has been emphasized in many studies. However, it is still limited in terms of the understanding of slope morphological differentiation on the overall and local failure patterns in slopes, as well as on the acquisition method of hydrological dynamics. In this study, rainfall characteristics and slope surface morphological differences were introduced. Geoelectric and environmental factors were monitored. On this basis, apparent resistivity corrected by seasonal temperature and its relationship with soil water content was calibrated. The water migration characteristics and potential failure patterns of three slope morphologies were evaluated. The results are: (i) the improved resistivity method can better reflect the water flow movement within the slope, and it performs well after being corrected by temperature; (ii) the characteristics of surface runoff and water infiltration are directly affected by the cumulative rainfall value, and especially when the cumulative rainfall is >70 mm threshold, the surface runoff quickly infiltrates into the deep of the slope along the preferential paths; (iii) the interception ability of loess slope morphology to the surface runoff is concave slope > convex slope > linear slope; (iv) with the continuous rainfall, the convex surface of a slope is prone to be damaged by saturated mud flow. When the cumulative rainfall threshold is 70 mm, the preferential flow is easily excited on the concave surface of the slope, resulting in local collapse at the slope toe and mid-deep landslides.

Coal mining inevitably brings some negative impacts, such as surface subsidence, aquifer breakage, and land degradation, to the eco-geological environment in the mining area. Among these impacts, coal mining-induced ground deformation is the most serious and has threatened the geological, ecological, and human settlement securities of mining areas. Efforts existing in the literature apply to ground deformation identification in mined-out areas at the meso-/micro and short-time scales. However, when looking back at coal mining history, there are few ways to quickly and accurately quantify ground deformation at the regional and long-time scales. In this context, we propose a method for identifying ground deformation patterns in coal mining areas using historical high-precision digital elevation models (DEMs), including data preprocessing, DEM subtraction operations, interpretation, and fitting correction. This method was applied to the Yulin National Energy and Chemical Base and successfully identified the ground deformation characteristics of the Yulin coal mining area from 2015 to 2019. By determining surface subsidence displacement, excavation depth, stacking height, and the position of the goaf suspended roof area, the objective situation of ground deformation in Yulin mining area was obtained, and the mining methods and distribution characteristics of different surface deformations were analyzed and determined. The research results are of great significance for the development of mineral resources in mining areas, reducing geological disaster risks, protecting the ecological environment, and achieving the goal of coordinated development in mining areas.

Loess regions face significant challenges in quantifying hydrological processes and assessing geological environmental risks due to the prevalent development of preferential pathways and the limitations of existing monitoring technologies. To advance this knowledge, this study presents an improved electrical resistivity tomography (ERT) device, specifically designed for loess moisture observations. By refining the testing principle, power supply mode, and data collection method within the existing ERT framework, the new device offers unmanned operation, automatic data acquisition, remote transmission, and cost efficiency. It effectively tracks water movement and groundwater level fluctuations across various hydrological conditions, supporting long-term online monitoring of hydrological processes of loess slopes. Through the analysis of monitoring data and classification of 12 observed preferential flow types, water movement in loess systems can be generalized into four general patterns: uniform infiltration, preferential infiltration, inflowing diffusion, and lateral flow. This generalized scheme provides a simplified modeling approach for other researchers to quantify slope hydrodynamics and to assess geological safety risks involving preferential flow. Based on these insights and field investigations, a conceptual framework is proposed to elucidate the seepage-structure synergistic initiating mechanism of loess landslides. This framework suggests that water entry and movement patterns within the slope depend on the slope geological structure related to preferential pathways and the prevailing hydrological scenarios. Landslide occurs as the result of the progressive failure and reciprocal evolution between the slope hydrological environments and geological structure, which may also pose potential eco-hydrological risks. The outcome advances the development of slope hydrological monitoring technology and enhances the understanding of water movement laws and the associated geological environmental risks in loess slope systems, which is of vital importance to the early warning methods of loess landslides that account for preferential flow and for theoretical modeling of preferential flow in related disciplines.

Azarshahr County in the northwest of Iran is predominantly covered by Azarshahr travertine, a prevailing sedimentary rock. This geological composition has led to extensive open-pit mining activities, particularly in the western and southwestern parts of the county. The rock's drillability and resistance to excavation play a pivotal role in determining its overall durability and hardness, crucial factors that influence the mining process. These attributes are intimately tied to the compressive strength of the rock. Accurate assessment of rock strength is vital for devising reliable excavation methodologies at mining sites. However, conventional approaches for analyzing rock strength have limitations that undermine the precision of strength estimations. In response, this study endeavors to leverage artificial intelligence techniques, specifically the Multilayer Perceptron (MLP), to enhance the prediction of travertine's compressive strength. To formulate a robust model, a comprehensive database containing data from 150 point-load index (I_s) tests on Azarshahr travertine was compiled. This dataset serves as the foundation for the development of the MLP-based predictive model, which proves instrumental in projecting rock compressive strength. The model's accuracy and efficacy were rigorously assessed using the Receiver Operating Characteristic (ROC) curve, employing both training and testing datasets. The modeling outcomes reveal impressive results. The estimated R-squared coefficient attained an impressive value of 0.975 for axial strength and 0.975 for diametral strength. The overall accuracy, as indicated by the Area Under the Curve (AUC) metric, stands at an impressive 0.968. These exceptional performance metrics underscore the efficacy of the MLP model in accurately predicting compressive strength based on the point-load index of samples. The implications of this study are substantial. The predictive model, empowered by the MLP approach, has profound implications for excavation planning and drillability assessment within the studied region's travertine deposits. By facilitating accurate forecasts of rock strength, this model equips mining endeavors with valuable insights for effective planning and execution.