Assessment of the Ecological Risks of Landslide Damages in the Carpathian Region

The dynamism of the landslides within the Carpathian region of Ukraine is because of the difficult engineering and geological conditions. High landslide den sity and significant population density contribute to the fact that environmental parameters worsen and require rational management. Permanent natural factors like clay flysch formation, fault tectonics, high seismic activity, and dense network of rivers mostly facilitate the active development of landslides in the Carpathian region. However, it is triggered by extreme long-term precipitation. The numerical parameters of population density, the landslide damage coefficient, and the predictive range of landslide intensification were selected to assess the ecological risk of damages in the area. The landslide dam age coefficient characterizes the tendency of the area to landslide development, considering all the factors contributing to the landslides. Risk, as a multifunctional calculated complex, includes the calculation of damage, according to which we can assess the possibility of risk for the human being while assuming the equal distribution of the population within the study area. The integral components of the risk are calculated based on the data gathered to assess the growth of risks in the future, considering the area distribution and predictive time series of the landslide intensification. This analysis has identified engineering and geological areas having the greatest risk to human life.

Decadal Evolution of Landslides in the Siwalik Zone: a Case Study of Babai Watershed, Nepal

The Siwalik zone of the Nepal Himalaya is highly sensitive to landslides. The study of landslides in the catchment scale gives the basic concept of the overall landslides of the typical zone. In this study, the decadal evolution trend of the four largest landslides of the Babai River watershed was evaluated. The Landsat, Sentinel-2, and Google Earth imageries were used to obtain the physical data of the landslide from 2010 to 2019. The area, total length, and width of scar toe, and the body of landslides were obtained from the images. The rainfall data of two stations was used to evaluate the role of rainfall in the landslide development and evolution process. The trend of rainfall and area of landslides was not the same but the development process of all four landslides was more or less similar. The area of landslides fluctuated till 2014 but suddenly increased after 2015. The landslide area was highest in 2017 and moderately changed in 2018 and 2019. The landslides showed dynamic behavior in a decade with their typical expanding, widening, and reducing characters.

Monitoring and Data Analysis in Small-Scale Landslide Physical Model

Physical modeling of landslides using scaled landslide models began in the 1970s in Japan at scaled natural slope physical models. Laboratory experiments of landslide behavior in scaled physical models (also known as flume or flume test) started in the 1980s and 1990s in Canada, Japan, and Australia under 1 g conditions. The main purpose of the landslide physical modeling in the last 25 years was research of initiation, motion, and accumulation of fast flow-like landslides caused by infiltration of water in a slope. In October 2018, at the Faculty of Civil Engineering University of Rijeka, started a four-year research project “Physical modeling of landslide remediation constructions’ behavior under static and seismic actions” funded by the Croatian Science Foundation. This paper presents an overview of the methods and monitoring equipment used in the physical models of a sandy slope exposed to artificial rainfall. Landslide development was monitored by observation of volumetric water content and acceleration as well as by observations of surface displacement by means of high-speed stereo cameras, terrestrial laser scanning, and structure-from-motion photogrammetry. Some of the preliminary results of the initial series of experiments are presented, and advantages and disadvantages of the used equipment are discussed.

Landslide Susceptibility Mapping Combining Information Gain Ratio and Support Vector Machines in the Three Gorges Reservoir Area, China

<p>The landslide development laws vary in different landslide-prone areas, hence the susceptibility models often perform in varied ways in different regions. Due to the periodic regulation of reservoir water level, a large number of landslides occur in the Three Gorges Reservoir area (TGRA). These landslides seriously threaten the safety of local residents and their property. It is crucial to find the model that can generate a landslide susceptibility map with higher accuracy in the TGRA. The main objective of this study was to explore the preference of machine learning models for landslide susceptibility mapping in the TGRA.</p><p>The Wushan segment of TGRA was selected as a case study, which is located in the middle reaches of the TGRA, the southwest of China. In this study, 165 landslides were identified and 14 landslide causal factors were constructed from different data sources at first, including altitude, slope, aspect, curvature, plan curvature, profile curvature, stream power index, topographic wetness index (TWI), terrain roughness index, lithology, bedding structure, distance to faults, distance to rivers, and distance to gully. Subsequently, multicollinearity analysis and information gain ratio model were applied to select landslide causal factors. After removing five factors (altitude, TWI, profile curvature, plan curvature, curvature), the landslide susceptibility mapping using the calculated results of four models, which were support vector machines (SVM), artificial neural networks, classification and regression tree, and logistic regression. Finally, the accuracy of the four models was evaluated and compared using the accuracy statistic methods and the receiver operating characteristic (ROC). The results of accuracy analysis showed that the SVM model performed the best. At the same time, the SVM performance behavior for susceptibility modelling in other areas were collected. In these regions, the accuracy of SVM was always larger than 0.8. We could see that SVM performed acceptably in different regions, and thus it can be used as a recommended model in TGRA and other landslide-prone regions.</p><p>In this study area, a total of 62% of the landslides were within 300 m from the Yangtze River, and the distance to rivers was the most important factor. The impoundment of the TGRA impacted the landslide development in three aspects: (1) the long-term immersion of reservoir water gradually reducing the strength of rock (soil) at the saturated zone (mostly near the Yangtze river), reducing the resistance force of landslide; (2) the strong dynamic action of water enhancing the lateral erosion on the bank slope, changing the slope shape, and thus reducing the slope stability; (3) the periodic fluctuation of the reservoir water making the self-weight, static, and dynamic water pressure of the landslide change, which could increase the resistance force or reduce the sliding force of the landslide and even cause overall instability and damage. Hence, in order to reduce the losses caused by landslides in TGRA, we should pay more attention to the early warning of reservoir bank landslides.</p>

Line length balancing to evaluate multi-phase submarine landslide development: an example from the Storegga Slide, Norway

This study shows how simple structural restoration of a discrete submarine landslide lobe can be applied to large-scale, multi-phase examples to identify different phases of slide-lobe development and evaluate their mode of emplacement. We present the most detailed analysis performed to date on a zone of intense contractional deformation, historically referred to as the compression zone, from the giant, multi-phase Storegga Slide, offshore Norway. 2D and 3D seismic data and bathymetry data show that the zone of large-scale (>650 m thick) contractional deformation can be genetically linked updip with a zone of intense depletion across a distance of 135 km. Quantification of depletion and accumulation along a representative dip-section reveals that significant depletion in the proximal region is not accommodated in the relatively mild amount (c. 5%) of downdip shortening. Dip-section restoration indicates a later, separate stage of deformation may have involved removal of a significant volume of material as part of the final stages of the Storegga Slide, as opposed to the minor volumes reported in previous studies.