Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm

Li, Yanyan; Wang, Honggang; Chen, Jianping; Shang, Yanjun

doi:10.3390/w9090669

Cited by 21 publications

(16 citation statements)

References 43 publications

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“…The qualitative model assigns a weight (0-1) to each debris flow triggering factor based on expert experience and knowledge or heuristics to assess the DFS [9]. Common qualitative analysis methods include fuzzy logic [10], analytic hierarchy [11] and network analysis [12] and so on. While these models have achieved a lot in the study of debris flows, they still suffer for some shortcomings, such as a high degree of subjectivity and limited applicability to specific areas [13].…”

Section: Introductionmentioning

confidence: 99%

Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China

et al. 2019

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Debris flows have been always a serious problem in the mountain areas. Research on the assessment of debris flows susceptibility (DFS) is useful for preventing and mitigating debris flow risks. The main purpose of this work is to study the DFS in the Shigatse area of Tibet, by using machine learning methods, after assessing the main triggering factors of debris flows. Remote sensing and geographic information system (GIS) are used to obtain datasets of topography, vegetation, human activities and soil factors for local debris flows. The problem of debris flow susceptibility level imbalances in datasets is addressed by the Borderline-SMOTE method. Five machine learning methods, i.e., back propagation neural network (BPNN), one-dimensional convolutional neural network (1D-CNN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) have been used to analyze and fit the relationship between debris flow triggering factors and occurrence, and to evaluate the weight of each triggering factor. The ANOVA and Tukey HSD tests have revealed that the XGBoost model exhibited the best mean accuracy (0.924) on ten-fold cross-validation and the performance was significantly better than that of the BPNN (0.871), DT (0.816), and RF (0.901). However, the performance of the XGBoost did not significantly differ from that of the 1D-CNN (0.914). This is also the first comparison experiment between XGBoost and 1D-CNN methods in the DFS study. The DFS maps have been verified by five evaluation methods: Precision, Recall, F1 score, Accuracy and area under the curve (AUC). Experiments show that the XGBoost has the best score, and the factors that have a greater impact on debris flows are aspect, annual average rainfall, profile curvature, and elevation.

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Section: Introductionmentioning

confidence: 99%

Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China

et al. 2019

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“…The RF model, which can analyze the importance of classification features and determine the weight of each controlling factor, is a classification model that is composed of many decision trees [ 54 , 55 ]. The RF model includes two main kinds of algorithms: the GINI index algorithm and the out-of-bag (OOB) error rate replacement algorithm.…”

Section: Methodsmentioning

confidence: 99%

A Method for Improving Controlling Factors Based on Information Fusion for Debris Flow Susceptibility Mapping: A Case Study in Jilin Province, China

Dou

Qin

Zhang

et al. 2019

Entropy

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Debris flow is one of the most frequently occurring geological disasters in Jilin province, China, and such disasters often result in the loss of human life and property. The objective of this study is to propose and verify an information fusion (IF) method in order to improve the factors controlling debris flow as well as the accuracy of the debris flow susceptibility map. Nine layers of factors controlling debris flow (i.e., topography, elevation, annual precipitation, distance to water system, slope angle, slope aspect, population density, lithology and vegetation coverage) were taken as the predictors. The controlling factors were improved by using the IF method. Based on the original controlling factors and the improved controlling factors, debris flow susceptibility maps were developed while using the statistical index (SI) model, the analytic hierarchy process (AHP) model, the random forest (RF) model, and their four integrated models. The results were compared using receiver operating characteristic (ROC) curve, and the spatial consistency of the debris flow susceptibility maps was analyzed while using Spearman’s rank correlation coefficients. The results show that the IF method that was used to improve the controlling factors can effectively enhance the performance of the debris flow susceptibility maps, with the IF-SI-RF model exhibiting the best performance in terms of debris flow susceptibility mapping.

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“…e Bayesian network model for debris flow prediction was proposed by Banihabib et al [10], and this research indicated that average basin elevation, watershed area, current rainfall, and discharge of 1 day ago are the effective predictors in forecasting debris flows. e application of the fuzzy C-means algorithm in the risk assessment of debris flow has also achieved more reliable results [11].…”

Section: Introductionmentioning

confidence: 99%

Debris Flow Risk Assessment Method Based on Combination Weight of Probability Analysis

Zhang

et al. 2021

Advances in Civil Engineering

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Risk assessment of debris flow is conducted by multicriteria decisions. Based on the shortcomings of the existing methods in determining the weight of assessment factors, this paper proposes a new approach to conduct a risk assessment of debris flow. This new approach regards the weight of factors as a uniform random variable, whose bounds could be determined by the equal weight method, maximal deviation method, and entropy method. The results of this new approach are obtained by Monte Carlo simulation. According to the risk of 72 debris flows collected in Beichuan, Sichuan, China, this new approach proves convergent. It is suggested that the minimum sample amount of Monte Carlo simulation should be 63095. The result also demonstrates that sorted results with different weights of factors vary a lot, so it is not convincing to sort samples with a specific weight.

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Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm

Cited by 21 publications

References 43 publications

Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China

Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China

A Method for Improving Controlling Factors Based on Information Fusion for Debris Flow Susceptibility Mapping: A Case Study in Jilin Province, China

Debris Flow Risk Assessment Method Based on Combination Weight of Probability Analysis

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