This paper presents a comprehensive review of earthquake-induced soil liquefaction hazards across various re- gions of India, emphasizing their connection with major seismic geohazards. India, with a population of approximately 1.45 billion, is characterized by a diverse geological and geotectonic setup and heightened seismicity in the subcontinent, necessitating robust construction techniques for long-term preparedness and mitigation for any impending earthquakes. This study systematically compiles and evaluates liquefaction sus- ceptibility across pan-India through different zones, including northern, southern, eastern, western, and north- eastern regions, in accordance with the four seismic zones of India. The data of standard penetration test (SPT) and cone penetration test (CPT) have been employed as the primary geotechnical methods to assess liquefaction potential, supported by geophysical tests and field observations. The factor of safety (FOS) was calculated to determine the severity of liquefaction susceptibility. In the north zone, areas like Srinagar show high liquefaction potential, especially near water bodies, while Chandigarh exhibits low susceptibility. The south zone's Chennai displays severe liquefaction risk along its eastern coastal parts, whereas south-western areas remain relatively safe. The east zone, particularly Bihar, is highly vulnerable, with districts like Darbhanga experiencing severe liquefaction due to frequent seismic activity. In the north-east zone, Guwahati, located in seismic zone V, shows high susceptibility, exacerbated by its proximity to the Brahmaputra River. The north-west zone's Kutch region in Gujarat is notably susceptible due to past seismic events and loose sandy soils, while urban areas like Mumbai show lower risk due to deeper bedrock conditions. Overall, Seismic Zone II, including regions in south and parts of the west zone, is identified as the safest, while seismic zone V, particularly in the northeast, north, and parts of the west, is most at risk. The findings underscore the urgency of assessing liquefaction as a critical seismic geohazard, necessitating robust soil testing, disaster-resilient infrastructure, and ground improvement techniques in vulnerable zones.

Instability of surrounding rock in deep roadways is a key issue in the research on safe and sustainable mining in coal mines. The development of rock cracks and the expansion of plastic zones directly affect roadway stability, and are likely to lead to disasters such as large deformations, roof caving, and sidewall spalling. This paper focuses on the mechanical mechanism of crack development in the surrounding rock of deep roadways. By establishing a mechanical analysis model, conducting discrete element numerical simulations, and analyzing the engineering case studies, the study extensively investigates the progression of crack development and the propagation of the plastic zone in the surrounding rock of roadways. The study clarifies the stress evolution characteristics of the surrounding rock, revealing the mechanical mechanism by which stress drives crack development, leading to the macroscopic instability of the surrounding rock. Additionally, it identifies the relationship between crack development and the distribution of plastic zones in the surrounding rock. The study emphasizes that the damage to the surrounding rock in the disturbed area near the deep roadway aligns with the direction of the minimum principal stress (σ3). Moreover, the extension direction of the plastic zone closely corresponds to the direction of propagation of tensile cracks. A risk zoning method for rock layer instability was proposed based on the characteristics of crack development and plastic zone distribution in the surrounding rock. The reliability of the research results was validated through engineering case studies. The research findings have significant theoretical and engineering implications, offering insights into the process of surrounding rock instability in deep roadways and providing a basis for formulating effective control strategies.

Landslides are highly destructive geohazard that occurs in various parts of the world, leading to environmental damage, loss of human lives, and destruction of properties. This study employed a multicriteria decision analysis framework and a GIS-based methodology to develop a landslide susceptibility map in the Telemt District in northwest Ethiopia's highlands. Ground truth data and satellite imagery were used in the mapping procedure. Nine criteria were evaluated using the analytical hierarchy process: lithology, lineament density, rainfall, river distance, slope, aspect, NDVI, curvature, and land use/land cover. We classed and weighted each theme component appropriately. The results of the study show that the landslide susceptibility zones were classified as follows: very low (18.4%), low (25.67%), moderate (25.81%), high (20.09%), and very high (10.03%) of the overall landslide area. The research area's northern and western regions, which were distinguished by moderate slopes, flat terrain, and alkaline basaltic rocks, were mostly home to the very low and low landslide susceptibility zones, which accounted for around 44.07% or 1356.14 km2. In contrast, the southern, eastern, and central re- gions of the research area, which were distinguished by steep slopes, high rainfall, and hilly terrain were home to the high to very high landslide susceptibility zones, which covered around 30.1% or 925.63 km2. The accuracy of the areas predicted to be susceptible to landslides was validated by comparing them with known landslide lo- cations using the ROC tool in ArcGIS. The AUC results for the AHP model were found to be 74%, suggesting a strong performance. The results of this study will provide important insights into landslide susceptibility for decision-making, rehabilitation efforts, mitigation strategies, and land use planning activities in the region.

The toppling stability is influenced by hydrogeology. However, the correlation between toppling and ground- water is complex and remains poorly be understood compared to the relationship between slides and ground- water. In this study, a geo-electrical investigation of the deep-seated Shidaguan toppling in southwestern China was conducted between April 2019 and May 2020. The material types of the toppling were determined and a morphological structural model was established based on drilling and apparent resistivity data. Time-lapse electrical resistivity tomography (ERT) was used to monitor the internal water flow processes within a large- scale deep-seated toppling. The processes of surface water infiltration and internal migration were captured, and a hydrological-mechanical coupling model was established. The model reveals that rainfall infiltrates the toppling body through priority flow paths, such as tension cracks, flexural fracture zones, and sliding surfaces, which leads to a reduction in shear strength along the basal surface. Furthermore, critical areas for monitoring and early warning of toppling were identified based on the evolution of deformation and water migration.

Time-dependent assessment of dynamic instability, rockburst phenomena, and potential collapse of unstable rocky reservoir banks presents the critical engineering challenges under multiphase hydro-mechanical coupling effects. While existing research has primarily focused on reservoir bank instability under static water level conditions, it has largely overlooked the process of time-dependent material degradation in stability assessment frameworks. This study addresses this knowledge gap through an integrated approach combining numerical modeling and experimental investigation. A finite element model was developed based on the Jianchuandong Rock Mass (JRM), incorporating hydrostatic pressure variations and strength reduction techniques. Com- plemented by controlled dry-wet cycling experiments simulating reservoir water fluctuations, the research quantitatively evaluates the coupled effects of hydraulic variations and progressive rock deterioration. The re- sults reveal significant temporal coupling between water level fluctuations and rock mass degradation, which mutually accelerates the destabilization process and ultimately leads to instability in the fifth hydrological year. The findings demonstrate that the proposed framework effectively captures the hydro-mechanical coupling mechanisms underlying reservoir bank behavior. This multi-field coupled analysis methodology achieves marked advancements beyond traditional static approaches through explicit consideration of time-dependent geotech- nical mass deterioration characteristics, providing technical support for the lifecycle assessment of long-term operation water conservancy projects.

Earthquakes are extremely destructive natural disasters, and accurately forecasting earthquakes is of great sig- nificance in reducing losses caused by earthquakes. To improve the accuracy of earthquake forecasting, we propose a novel method that integrates an improved artificial rabbit optimization algorithm (IARO), variational mode decomposition (VMD), and deep learning, named IARO-VMD-DFGNet, for earthquake time series fore- casting. Facing the challenges caused by the need for manual setting of VMD parameters, we propose the IARO to optimize the parameters of VMD, thus avoiding errors caused by manual parameter setting. Additionally, we also propose a data-driven deep learning model, DFGNet, for forecasting decomposed data. The data used in this study are earthquake catalogs, which include five variables: timestamp, longitude, latitude, depth, and magni- tude. Each variable is independently decomposed and forecasted. The performance of the model is evaluated using four metrics: mean squared error, mean absolute error, relative standard error, and root mean square error. Experimental results from four different earthquake catalogs demonstrate that the proposed model outperforms several other popular time series forecasting models. It achieves average reductions of 24.6%, 18.5%, 15.2%, and 13.7% across the four evaluation metrics, demonstrating significant competitive advantages. Therefore, applying IARO-VMD-DFGNet to earthquake forecasting is of great significance in reducing the harm caused by earthquakes.