Regional Landslide Hazard Warning and Risk Assessment

A Data-Driven Model for Spatial Shallow Landslide Probability of Occurrence Due to a Typhoon in Ningguo City, Anhui Province, China

From 9 to 11 August 2019, the southeast coastal areas of China were hit by Typhoon Lekima, which caused a large number of shallow landslides. The typhoon resulted in a maximum rainfall of 402 mm during 3 days in Ningguo City. In this study, satellite images were acquired before and after the rainfall and visual interpretation was used to identify 414 shallow landslides in Ningguo City, and a complete database of shallow landslides caused by the typhoon-induced rainfall in Ningguo City was created. Nine landslide-influencing factors were selected—elevation, slope, aspect, strata, distance to faults, distance to rivers, distance to roads, normalized vegetation difference index, and rainfall—and the relationships between the rainfall-induced landslide distribution and the influencing factors were analyzed. The Bayesian probability method was combined with a logistic regression model to establish a landslide probability map for the study area. The real probabilities of landslide occurrence in the study area under five different rainfall conditions were calculated, and probability maps of landslide occurrence were drawn. The results of this study provide a reference for disaster prevention and reduction of typhoon rainstorm landslides in the southeast coastal areas of China and a future basis for decision making by the Ningguo government departments before a typhoon rainstorm occurs.

Differentiation Rule and Driving Mechanisms of Collapse Disasters in Changbai County

The differentiation rule and driving mechanisms of collapse disasters in various regions are unclear, and the results from existing methods of research are not sufficiently scientific. To reveal the nature of collapse disasters, this study utilized data from the 1:50,000 geological disaster investigation results database, 1:50,000 topographic data, and TM images. Topography, human activity intensity, rock mass structure, hydrological conditions, vegetation status, and meteorological conditions were used as indicators in the DEA model to analyze their validity and to explore the differentiation law and driving mechanisms of the highway slope along the YaLu river, a location of frequent geological disasters in Changbai County. In the analysis process, each index was quantitatively graded, i.e., the number of disaster points corresponding to each index was used as an input index, and the number of disaster points and the scale and stability of disaster points corresponding to the graded quantitative index were used as the output indexes. The results of the analysis of the study area indicate that there are significant differences in geological disasters due to different regional characteristics. We carried out three evaluations and performed spatial superposition analysis of the indicators corresponding to the effective values and the regional collapse points. The driving factors of collapse disasters can be divided into three categories, namely the impact of human activities, rainfall, and gravity stress. The GIS analysis and mapping found that the collapse points located to the south of the Grand Canyon of Changbai County were primarily affected by rainfall. Additionally, the areas affected by activity intensity are mostly concentrated in county towns with concentrated populations and road slopes.

Marine oil spill risk mapping for accidental pollution and its application in a coastal city

Accidental marine oil spill pollution can result in severe environmental, ecological, economic and other consequences. This paper discussed the model of Marine Oil Spill Risk Mapping (MOSRM), which was constructed as follows: (1) proposing a marine oil spill risk system based on the typical marine oil spill pollution accidents and prevailing risk theories; (2) identifying suitable indexes that are supported by quantitative sub-indexes; (3) constructing the risk measuring models according to the actual interactions between the factors in the risk system; and (4) assessing marine oil spill risk on coastal city scale with GIS to map the overall risk. The case study of accidental marine oil spill pollution in the coastal area of Dalian, China was used to demonstrate the effectiveness of the model. The coastal areas of Dalian were divided into three zones with risk degrees of high, medium, and low. And detailed countermeasures were proposed for specific risk zones.