Risk Assessment of Debris Flow in a Mountain-Basin Area, Western China

Abstract

Debris flow risk comprehensively reflects the natural and social properties of debris flow disasters and is composed of the risk of the disaster-causing body and the vulnerability of the carrier. The Bailong River Basin (BRB) is a typical mountainous environment where regional debris flow disasters occur frequently, seriously threatening the lives of residents, infrastructure, and regional ecological security. However, there are few studies on the risk assessment of mountainous debris flow disasters in the BRB. By considering a complete catchment, based on remote sensing and GIS methods, we selected 17 influencing factors, such as area, average slope, lithology, NPP, average annual precipitation, landslide density, river density, fault density, etc. and applied a machine learning algorithm to establish a hazard assessment model. The analysis shows that the Extra Trees model is the most effective for debris flow hazard assessments, with an accuracy rate of 88%. Based on socio-economic data and debris flow disaster survey data, we established a vulnerability assessment model by applying the Contributing Weight Superposition method. We used the product of debris flow hazard and vulnerability to construct a debris flow risk assessment model. The catchments at a very high-risk were distributed mainly in the urban area of Wudu District and the northern part of Tanchang County, that is, areas with relatively dense economic activities and a high disaster frequency. These findings indicate that the assessment results provide scientific support for planning measures to prevent or reduce debris flow hazards. The proposed assessment methods can also be used to provide relevant guidance for a regional risk assessment of debris flows in the BRB and other regions.

Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

“…Notably, the representation of identical candidate variables varies across the papers, reflecting the nuances of individual research intentions. Therefore, with reference to relevant studies [24,[78][79][80][81][82], the candidate variables from the 84 papers were systematically categorized into 12 groups: topography factors, morphology factors, geomorphology factors, geology factors, meteorology factors, hydrology factors, soil factors, vegetation factors, fire factors, material source factors, human activity factors, and past debris flow characteristic factors (Table 3).…”

Risk Assessment and Analysis of Its Influencing Factors of Debris Flows in Typical Arid Mountain Environment: A Case Study of Central Tien Shan Mountains, China

“…After collecting the above data, we carried out a series of data preprocessing steps, including the digitization of data products, rasterization of elements, stitching, projection transformation, resampling and cropping [29]. Eventually, we obtained the dataset under the WGS84 coordinate system.…”

“…Additionally, broken rock enters the channel and replenishes the debris flow source. Based on the study [29,33], the lithology of the study area is categorized into four types: very soft rock, soft rock, hard rock, and very hard rock, based on the engineering geological rock group (Table 3). After this, we utilized the zoning statistics tool of ArcGIS (https://www.esri.com/zh-cn/home (accessed on 15 May 2022)) to calculate the factor values for watershed units for the hazard assessment factor, which were then normalized and input into the machine learning model.…”

A GIS-Based Approach for Shallow Landslides Risk Assessment in the Giampilieri and Briga Catchments Areas (Sicily, Italy)

“…These initiatives provide accurate geospatial information about local rainfall, land cover, digital elevation models, and landslides [23][24][25]. Despite technological advances and innovative approaches [26,27], shallow landslide risk assessment tools often face high levels of uncertainty, connected primarily with the difficulty of modelling a highly complex physical process. Anticipated future climatic and socioeconomic changes, including increased urbanisation and resulting land use changes, contribute to further complicating uncertainties in predictive scenarios [28].…”