Deep excavated expansive soil slopes are influenced by multiple factors, including precipitation, groundwater, geological structure, and support measures, resulting in complex spatiotemporal heterogeneity in deformation. Using the slope of Taocha canal segment in the middle route of the South-to-North Water Diversion Project as a case study, the spatiotemporal deformation characteristics were analyzed. Data mining methods, including variational modal decomposition, weighted multiscale local outlier factor, and clustering analysis, were applied. The temporal variation of deformation was analyzed, and spatial distribution characteristics were identified. The influence mechanisms of factors such as precipitation and groundwater on the trend, periodic, and fluctuating components of deformation were explained. Deformation measuring points were clustered, and potential sliding surfaces and sliding bodies were speculated. The deformation mechanism was discussed, and reinforcement measures were proposed. The results indicate that slope deformation exhibits significant trend changes, as well as seasonal and intermittent fluctuations. Deformation in the lower part is relatively large and gradually decreases upwards. The depth of the significant deformation in the upper part is 3 m, located within the climate-influenced layer. Deformation in the central part is influenced by groundwater fluctuation and dense fissure zones, with a significant deformation depth of up to 11 m. Deformation in the lower part is limited by the retaining system and occurs only in the shallow layer. Perched water in the upper part is replenished by rainwater, causing large fluctuation depths and resulting in deep deformation in the middle and upper parts, and there is a certain degree of deformation within the depth of 16.5 m. The potential sliding surface is a polyline. Due to the influence of groundwater, densely fissure zones, and retaining system, the leading edge is approximately horizontal. Drainage wells should be installed to drain groundwater to reduce the impact of groundwater fluctuations on the swelling-shrinkage deformation of expansive soil. The research results can provide technical support for the operation management and reinforcement disposal of deep excavated expansive soil slopes.

Since the impoundment of Three Gorges Reservoir (TGR) in 2003, numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall. One case is the Outang landslide, a large-scale and active landslide, on the south bank of the Yangtze River. The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics. Data mining technology, including the two-step clustering and Apriori algorithm, is then used to identify the dominant triggers of landslide movement. In the data mining process, the two-step clustering method clusters the candidate triggers and displacement rate into several groups, and the Apriori algorithm generates correlation criteria for the cause-and-effect. The analysis considers multiple locations of the landslide and incorporates two types of time scales: longterm deformation on a monthly basis and short-term deformation on a daily basis. This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors. The data mining results reveal different dominant triggering factors depending on the monitoring frequency: the monthly and bi-monthly cumulative rainfall control the monthly deformation, and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide. It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslideprone areas. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Seismic events remain a significant threat, causing loss of life and extensive damage in vulnerable regions. Soil liquefaction, a complex phenomenon where soil particles lose confinement, poses a substantial risk. The existing conventional simplified procedures, and some current machine learning techniques, for liquefaction assessment reveal limitations and disadvantages. Utilizing the publicly available liquefaction case history database, this study aimed to produce a rule-based liquefaction triggering classification model using rough set-based machine learning, which is an interpretable machine learning tool. Following a series of procedures, a set of 32 rules in the form of IF-THEN statements were chosen as the best rule set. While some rules showed the expected outputs, there are several rules that presented attribute threshold values for triggering liquefaction. Rules that govern fine-grained soils emerged and challenged some of the common understandings of soil liquefaction. Additionally, this study also offered a clear flowchart for utilizing the rule-based model, demonstrated through practical examples using a borehole log. Results from the state-of-practice simplified procedures for liquefaction triggering align well with the proposed rule-based model. Recommendations for further evaluations of some rules and the expansion of the liquefaction database are warranted.

The Tibetan Plateau, known as the world's Third Pole due to its high altitude, is experiencing rapid, intense climate change, similar to and even far more than that occurring in the Arctic and Antarctic. Scientific data sharing is very important to address the challenges of better understanding the unprecedented changes in the Third Pole and their impacts on the global environment and humans. The National Tibetan Plateau Data Center (TPDC, http://data.tpdc.ac.cn) is one of the first 20 national data centers endorsed by the Ministry of Science and Technology of China in 2019 and features the most complete scientific data for the Tibetan Plateau and surrounding regions, hosting more than 3,500 datasets in diverse disciplines. Fifty datasets featuring high-mountain observations, land surface parameters, near-surface atmospheric forcing, cryospheric variables, and high-profile article-associated data over the Tibetan Plateau, frequently being used to quantify the hydrological cycle and water security, early warning assessments of glacier avalanche disasters, and other geoscience studies on the Tibetan Plateau, are highlighted in this manuscript. The TPDC provides a cloud-based platform with integrated online data acquisition, quality control, analysis, and visualization capability to maximize the efficiency of data sharing. The TPDC shifts from the traditional centralized architecture to a decentralized deployment to effectively connect Third Pole-related data from other domestic and international data sources. As an embryo of data sharing and management over extreme environment in the upcoming big data era, the TPDC is dedicated to filling the gaps in data collection, discovery, and consumption in the Third Pole, facilitating scientific activities, particularly those featuring extensive interdisciplinary data use.

High-latitude regions are experiencing rapid and extensive changes in ecosystem composition and function as the result of increases in average air temperature. Increasing air temperatures have led to widespread thawing and degradation of permafrost which in turn has affected ecosystems, socioeconomics, and the carbon cycle of high latitudes. Here we overcome complex interactions among surface and subsurface conditions to map near-surface permafrost through decision and regression tree approaches that statistically and spatially extend field observations using remotely sensed imagery, climatic data, and thematic maps of a wide range of surface and subsurface biophysical characteristics. The data fusion approach generated medium-resolution (30-m pixels) maps of near-surface (within 1 m) permafrost, active-layer thickness, and associated uncertainty estimates throughout mainland Alaska. Our calibrated models (overall test accuracy of similar to 85%) were used to quantify changes in permafrost distribution under varying future climate scenarios assuming no other changes in biophysical factors. Models indicate that near-surface permafrost underlies 38% of mainland Alaska and that near-surface permafrost will disappear on 16 to 24% of the landscape by the end of the 21st Century. Simulations suggest that near-surface permafrost degradation is more probable in central regions of Alaska than more northerly regions. Taken together, these results have obvious implications for potential remobilization of frozen soil carbon pools under warmer temperatures. Additionally, warmer and drier conditions may increase fire activity and severity, which may exacerbate rates of permafrost thaw and carbon remobilization relative to climate alone. The mapping of permafrost distribution across Alaska is important for land-use planning, environmental assessments, and a wide-array of geophysical studies. (C) 2015 Elsevier Inc. All rights reserved.