China's Loess Plateau is both the largest and deepest loess deposit in the world, and it has long been one of the most severely eroded areas on Earth. Since the 1970s, numerous soil- and water-conservation practices have been implemented: terracing, planting of vegetation, natural vegetation rehabilitation, and check-dam construction. With the implementation of the Grain-for-Green Project in 1999, the Loess Plateau has become the most successful ecological restoration zone in China. However, these large-scale restoration measures and drought have significantly reduced both runoff and sediment from the Loess Plateau. This situation has both advantages and disadvantages for the lower Yellow River. Some local soil erosion has been successfully controlled, but the whole regional ecosystem remains very fragile. Therefore, it is necessary to balance each ecosystem service, for example, by determining the region's vegetation capacity and its spatial distribution for the sustainable development of the socioecolog...

Hydrogeomorphic Ecosystem Responses to Natural and Anthropogenic Changes in the Loess Plateau of China

Bioremediation mechanisms of combined pollution of PAHs and heavy metals by bacteria and fungi: A mini review.

Evaluation methods for assessing effectiveness of in situ remediation of soil and sediment contaminated with organic pollutants and heavy metals.

Landslide is one of the repeated geological hazards during rainy season, which causes fatalities, damage to property and economic losses in Korea. Landslides are responsible for at least 17% of all fatalities from natural hazards worldwide, and nearly 25% of annual casualties caused by natural hazards in Korea. Due to global climate change, the frequency of landslide occurrence has been increased and subsequently, the losses and damages associated with landslides also have been increased. Therefore, accurate prediction of landslide occurrence, and monitoring and early warning for ground movements are very important tasks to reduce the damages and losses caused by landslides. Various studies on landslide prediction and reduction in landslide damage have been performed and consequently, much of the recent progress has been in these areas. In particular, the application of information and geospatial technologies such as remote sensing and geographic information systems (GIS) has greatly contributed to landslide hazard assessment studies over recent years. In this paper, the recent advances and the state-of-the-art in the essential components of the landslide hazard assessment, such as landslide susceptibility analysis, runout modeling, landslide monitoring and early warning, were reviewed. Especially, this paper focused on the evaluation of the landslide susceptibility using probabilistic approach and physically based method, runout evaluation using volume based model and dynamic model, in situ ground based monitoring techniques, remote sensing techniques for landslide monitoring, and landslide early warning using rainfall and physical thresholds.

Landslide prediction, monitoring and early warning: a concise review of state-of-the-art

The main aim of this study was to apply and compare two GIS-based data mining models, namely support vector machine (SVM) by four kernel functions (linear-SVM, polynomial-SVM, radial basic function-SVM, and sigmoidal-SVM) and entropy models in landslide susceptibility mapping, in Shangzhou District, China. Initially, 145 landslide locations were mapped using early reports, aerial photographs, and supported by field surveys. Subsequently, landslides in the study area were divided randomly into training and validation datasets (70/30) using ArcGIS 10.0 software. In the current study, 14 landslide conditioning factors, namely, slope aspect, slope angle, profile curvature, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), normalized difference vegetation index (NDVI), distance from roads, distance from rivers, distance from faults, rainfall, and lithology, were exploited to detect the most susceptible areas. In the next step, landslide susceptibility maps generated by four types of SVM or entropy models were produced. Finally, validation of the landslide susceptibility maps produced by different models was evaluated using receiver operating characteristics (ROC) curves. The results showed that the entropy model exhibited the highest success rate (0.7610), followed by polynomial-SVM (0.7526), the sigmoidal-SVM (0.7518), radial basic function-SVM (0.7446), and linear-SVM (0.7390) models. Similarly, the ROC plots also showed that the prediction rates gave almost similar results. The entropy model had the highest prediction rate (0.7599), followed by polynomial-SVM (0.7259), sigmoidal-SVM (0.7203), radial basic function-SVM (0.7149), and linear-SVM (0.7009) models. Hence, it can be concluded that the five models used in this study gave close results, with the entropy model exhibiting best performance in landslide susceptibility mapping.

A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China

Major rock landslides are the main type of geological hazards in the mountainous areas of southwestern China, in which failure modes and behaviors are compounded. In this study, field surveys, centrifuge modelling, and numerical simulations were utilized to analyze the failure process of a steep-dip and layered carbonate slope which was induced by underground goaf. These included gravitational bending, interlayer faulting, topple failure of the upper blocks due to underground mining, shearing through the underlying slide-resistant rocks, and catastrophic avalanches. It was concluded that the bending and compaction of the weak interbedded coal seam had occurred on the slope with the effects of gravity. The support for the overburden rocks was lost following the underground mining activities, which led to bedding separation and the generation of cracks. The upper blocks acted on blocks at the toe, and shear failure at the toe was initiated when the force along the joints exceeded the anti-shear strength, which gradually formed an intact failure surface. Then, a rock slide took place which transformed into a catastrophic rock avalanche. The failure process was validated using a FLAC3D simulation and centrifuge testing.

Zhen Feng

Papers

Processes and behaviors of block topple avalanches resulting from carbonate slope failures due to underground mining