Application of the borderline-SMOTE method in susceptibility assessments of debris flows in Pinggu District, Beijing, China

Geographic information system models with fuzzy logic for susceptibility maps of debris flow using multiple types of parameters: a case study in Pinggu District of Beijing, China

“…The predictive performance of the susceptibility maps and the spatial correlation of debris flow gully with H and VH susceptibility with recorded debris flows illustrate that the assessment at regional scale using the proposed method is feasible. Compared with the previous results (Li et al, 2020b) based on grid units, the evaluation results are basically the same, but the model is more targeted at debris flow disasters for decision makers. Moreover, considering that the meaning of the factors used is clear and the data are easy to obtain, these conditions mentioned enable the model to be widely applied.…”

“…Zhang et al: GIS-models with fuzzy logic for susceptibility maps of debris flow al., 2021). As the capital of China, Beijing also has strong influence at home and abroad where geological disasters are widely concerned (Xie et al, 2004;Li et al, 2020b). With the deepening understanding of debris flow disaster and the updating of a database, a new and more accurate evaluation is also very necessary.…”

“…In the early days, the susceptibility assessment of debris flows was mainly qualitative research using geomorphological information (Guzzetti et al, 1999). In 1976, the United Nations commissioned the International Union of Engineering Geology to conduct a risk assessment of debris flows, which marked the beginning of research on the susceptibility assessment of debris flows as an important research direction for disaster prevention and prediction (Li et al, 2020b). Many methods and techniques have been proposed to evaluate debris flow susceptibility assessment based on different qualitative and quantitative approaches along with geo-environmental information (Liu and Wang, 1995), such as the analytic hierarchy process (Wu et al, 2016), logistic regression method (Regmi et al, 2013;Conoscenti et al, 2015), information value (Akbar and Ha, 2011;Melo et al, 2012), support vector machine (Pourghasemi et al, 2017), frequency ratio (FR) (Sun et al, 2018), certainty factor (CF) (Tsangaratos and Ilia, 2015), neural network (Lee et al, 2003;Liu et al, 2005) and Bayesian network algorithm (Liang et al, 2012;Tien Bui et al, 2012).…”

Susceptibility Assessment of Single Gully Debris Flow Based on AHP and Extension Method

“…Normally, debris flow formation involves three constraints: loose source material, topography, and rainfall [12]. The loose source material is the physical source of debris flow events and is linked to section lithology and geological structures by influencing the conflux mechanism [45]. Table 4 referenced the excessively used causative factors by various researchers for debris flow susceptibility assessment.…”

“…The eight most relevant influencing factors were preferred in this study, including slope (F1), TWI (F2), STI (F3), ground roughness (F4), basin area (F5), bending coefficient of the main channel (F6), source material (F7), and NDVI (F8) were selected as evaluation indicators in this study. These variables directly or indirectly impact the happening of debris flows and have been widely used in the assessment of debris flow susceptibility [45,53].…”