Distribution and Susceptibility Assessment of Collapses and Landslides in the Riparian Zone of the Xiaowan Reservoir

Abstract

The southwest alpine gorge region is the major state base of hydropower energy development in China and hence planned many cascading hydropower stations. After the reservoir impoundment, the intense water level fluctuations under the interaction of cascade dams operating and the mountainous flooding, usually cause bank collapse, landslide and debris flow hazards. The Xiaowan reservoir (XWR), for example, as the 'dragon head' meg reservoir located in the middle mainstream of Lancang River, have resulted in a series of geohazards during its building and operating. In this study, we investigated the number and surface area of collapses and landslides (CLs) occurred in the water level fluctuations zone (WLFZ) of XWR using remote sensing images of Gaofen-1 and Google Earth; evaluated the CLs susceptibility using information value method. The results presented that the total WLFZ area of 87.03 km 2 and 804 CLs masses with a total area of 1.98 km 2 were identified in the riparian zone of XWR. CLs mainly occurred at an elevation of 1190-1240 m, and the CLs density increased with an increase in altitude. The WLFZ with a slope gradient of 25°-45° is the main CLs distribution area that accounts for more than half of the total CLs area. The susceptibility assessment revealed that high and very high susceptibility zones are generally distributed along zones with an elevation of 1210-1240 m, a slope degree of 25°-45° and a slope aspect perpendicular to the direction of Lancang River. Furthermore, these susceptible zones are close in distance to the dam site and tend to be in the riparian zones with the formation lithology of Silurian strata. These results provide a valuable contribution to prevent and control geohazards in the XWR area. Moreover, this study offers a constructive sample of geohazards assessment in the riparian zone of large reservoirs throughout the mountains of southwest China.

Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

“…Enhancing this optimization significantly improves the speed and accuracy of susceptibility map production. In this study, we employ a novel combination of data and spatial features through nine machine-learning models, alongside geographic detectors, pioneering this approach based on the foundational work cited in references [6][7][8]. Although various optimization methods ranging from simple to complex models have been explored by scholars, often yielding improved results, our approach addresses the lack of spatial interpretation inherent in many machine learning models, thereby enhancing the practical application of these optimized factors.…”

“…In recent years, the frequency of landslides and slope instabilities has sharply increased, primarily due to the cumulative effects of rapidly changing climate conditions and heightened anthropogenic disturbances [4,5]. Landslide disasters are frequent in China, among which seismic landslides pose the most severe threats [6]. To more effectively address these disasters and promote sustainable development, the creation of Seismic Landslide Susceptibility Maps (SLSMs) is imperative [7].…”

The Analyses of Land Use and Prevention in High-Density Main Urban Areas under the Constraint of Karst Ground Subsidence: Study of Wuhan City, China

“…This study analyzes the susceptibility of KGS from three basic conditions: karst conditions (KCs), overburden conditions (OCs), and hydrodynamic conditions (HCs). Based on ignoring factors that have little impact on basic conditions, we further decompose basic conditions into six influencing factors: bedrock lithology (KC BL ), degree of karstification (KC DK ), thickness of overburden OC T , lithology and structure of overburden OC LS , distance between groundwater level and bedrock HC DLB , and variation in groundwater level HC VL [17,[50][51][52][53].…”

“…[12,13]. Several studies [14][15][16][17][18] have investigated the triggering mechanisms of KGS, using various analytical methods such as microgravity measurement, logistic regression analysis, and the information value method or experimental simulation, and found that factors such as unstable surface sediments, land development and utilization, poor surface drainage, and high surface slope can affect the occurrence of KGS. However, the above research primarily focuses on large areas and wide plain suburbs, emphasizing the KGS phenomenon's causal mechanisms and statistical laws.…”

Collapse Susceptibility Assessment in Taihe Town Based on Convolutional Neural Network and Information Value Method

“…With the development of the Geographical Information System (GIS) and Remote Sensing (RS) technology, geological hazard susceptibility assessments have also become more efficient and accurate, which greatly accelerates the research process in this field. The assessment methods of geological hazards mainly include the Analytic Hierarchy Process (AHP) [3][4][5][6][7], the Support Vector Machine (SVM) [8,9], the Information Value method (IV) [10,11], the Frequency Ratio method (FR) [4,12], the Weight of Evidence method (WOE) [4,13,14], Artificial Neural Network (ANN) [15][16][17], Random Forest (RF) [8,18] and a variety of model coupling methods [19][20][21][22].…”