Debris flow susceptibility mapping using frequency ratio and seed cells, in a portion of a mountain international route, Dry Central Andes of Argentina

Debris flow susceptibility mapping is considered to be useful for hazard prevention and mitigation. As a frequent debris flow area, many hazardous events have occurred annually and caused a lot of damage in the Sichuan Province, China. Therefore, this study attempted to evaluate and compare the performance of four state-of-the-art machine-learning methods, namely Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Boosted Regression Trees (BRT), for debris flow susceptibility mapping in this region. Four models were constructed based on the debris flow inventory and a range of causal factors. A variety of datasets was obtained through the combined application of remote sensing (RS) and geographic information system (GIS). The mean altitude, altitude difference, aridity index, and groove gradient played the most important role in the assessment. The performance of these modes was evaluated using predictive accuracy (ACC) and the area under the receiver operating characteristic curve (AUC). The results of this study showed that all four models were capable of producing accurate and robust debris flow susceptibility maps (ACC and AUC values were well above 0.75 and 0.80 separately). With an excellent spatial prediction capability and strong robustness, the BRT model (ACC = 0.781, AUC = 0.852) outperformed other models and was the ideal choice. Our results also exhibited the importance of selecting suitable mapping units and optimal predictors. Furthermore, the debris flow susceptibility maps of the Sichuan Province were produced, which can provide helpful data for assessing and mitigating debris flow hazards.

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COUNTY-WIDE DEBRIS-FLOW SUSCEPTIBILITY MAPPING IN COLORADO: PROGRAM OVERVIEW AND METHODS SUMMARY

10.1130/abs/2016am-283839 ◽

2016 ◽

Author(s):

Kevin McCoy ◽

◽

Francis Scot Fitzgerald ◽

Matthew L. Morgan

Keyword(s):

Debris Flow ◽

Susceptibility Mapping ◽

Debris Flow Susceptibility

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Spatial Predictions of Debris Flow Susceptibility Mapping Using Convolutional Neural Networks in Jilin Province, China

Water ◽

10.3390/w12082079 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2079

Author(s):

Yang Chen ◽

Shengwu Qin ◽

Shuangshuang Qiao ◽

Qiang Dou ◽

Wenchao Che ◽

...

Keyword(s):

Neural Networks ◽

Debris Flow ◽

Statistical Methods ◽

Convolutional Neural Networks ◽

Debris Flows ◽

Susceptibility Mapping ◽

Jilin Province ◽

Support Vector ◽

Validation Set ◽

Debris Flow Susceptibility

Debris flows are a major geological disaster that can seriously threaten human life and physical infrastructures. The main contribution of this paper is the establishment of two–dimensional convolutional neural networks (2D–CNN) models by using SAME padding (S–CNN) and VALID padding (V–CNN) and comparing them with support vector machine (SVM) and artificial neural network (ANN) models, respectively, to predict the spatial probability of debris flows in Jilin Province, China. First, the dataset is randomly divided into a training set (70%) and a validation set (30%), and thirteen influencing factors are selected to build the models. Then, multicollinearity analysis and gain ratio methods are used to quantify the predictive ability of factors. Finally, the area under the receiver operatic characteristic curve (AUC) and statistical methods are utilized to measure the accuracy of the models. The results show that the S–CNN model gets the highest AUC value of 0.901 in the validation set, followed by the SVM model, the V–CNN model, and the ANN model. Three statistical methods also show that the S–CNN model produces minimum errors compared with other models. The S–CNN model is hailed as an important means to improve the accuracy of debris–flow susceptibility mapping and provides a reasonable scientific basis for critical decisions.

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Comparison of different sampling strategies for debris flow susceptibility mapping: A case study using the centroids of the scarp area, flowing area and accumulation area of debris flow watersheds

Journal of Mountain Science ◽

10.1007/s11629-020-6471-y ◽

2021 ◽

Vol 18 (6) ◽

pp. 1476-1488

Author(s):

Rui-yuan Gao ◽

Chang-ming Wang ◽

Zhu Liang

Keyword(s):

Debris Flow ◽

Susceptibility Mapping ◽

Sampling Strategies ◽

Debris Flow Susceptibility

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Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway

Remote Sensing ◽

10.3390/rs12182933 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2933

Author(s):

Feng Qing ◽

Yan Zhao ◽

Xingmin Meng ◽

Xiaojun Su ◽

Tianjun Qi ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Debris Flow ◽

Debris Flows ◽

Susceptibility Mapping ◽

Support Vector ◽

Safe Operation ◽

Extreme Gradient Boosting ◽

Karakoram Highway ◽

Debris Flow Susceptibility

The China–Pakistan Karakoram Highway is an important land route from China to South Asia and the Middle East via Pakistan. Due to the extremely hazardous geological environment around the highway, landslides, debris flows, collapses, and subsidence are frequent. Among them, debris flows are one of the most serious geological hazards on the Karakoram Highway, and they often cause interruptions to traffic and casualties. Therefore, the development of debris flow susceptibility mapping along the highway can potentially facilitate its safe operation. In this study, we used remote sensing, GIS, and machine learning techniques to map debris flow susceptibility along the Karakoram Highway in areas where observation data are scarce and difficult to obtain by field survey. First, the distribution of 544 catchments which are prone to debris flow were identified through visual interpretation of remote sensing images. The factors influencing debris flow susceptibility were then analyzed, and a total of 17 parameters related to geomorphology, soil materials, and triggering conditions were selected. Model training was based on multiple common machine learning methods, including Ensemble Methods, Gaussian Processes, Generalized Linear models, Navies Bayes, Nearest Neighbors, Support Vector Machines, Trees, Discriminant Analysis, and eXtreme Gradient Boosting. Support Vector Classification (SVC) was chosen as the final model after evaluation; its accuracy (ACC) was 0.91, and the area under the ROC curve (AUC) was 0.96. Among the factors involved in SVC, the Melton Ratio (MR) was the most important, followed by drainage density (DD), Hypsometric Integral (HI), and average slope (AS), indicating that geomorphic conditions play an important role in predicting debris flow susceptibility in the study area. SVC was used to map debris flow susceptibility in the study area, and the results will potentially facilitate the safe operation of the highway.

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Development of a region-partitioning method for debris flow susceptibility mapping

Journal of Mountain Science ◽

10.1007/s11629-020-6497-1 ◽

2021 ◽

Vol 18 (5) ◽

pp. 1177-1191

Author(s):

Shuang-shuang Qiao ◽

Sheng-wu Qin ◽

Jing-bo Sun ◽

Wen-chao Che ◽

Jing-yu Yao ◽

...

Keyword(s):

Debris Flow ◽

Susceptibility Mapping ◽

Debris Flow Susceptibility

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A comparison of statistical and machine learning methods for debris flow susceptibility mapping

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-020-01851-8 ◽

2020 ◽

Vol 34 (11) ◽

pp. 1887-1907 ◽

Cited By ~ 1

Author(s):

Zhu Liang ◽

Chang-Ming Wang ◽

Zhi-Min Zhang ◽

Kaleem-Ullah-Jan Khan

Keyword(s):

Machine Learning ◽

Debris Flow ◽

Susceptibility Mapping ◽

Learning Methods ◽

Machine Learning Methods ◽

Debris Flow Susceptibility

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

Entropy ◽

10.3390/e21070695 ◽

2019 ◽

Vol 21 (7) ◽

pp. 695 ◽

Cited By ~ 2

Author(s):

Qiang Dou ◽

Shengwu Qin ◽

Yichen Zhang ◽

Zhongjun Ma ◽

Junjun Chen ◽

...

Keyword(s):

Debris Flow ◽

Information Fusion ◽

Slope Angle ◽

Controlling Factors ◽

Susceptibility Mapping ◽

Jilin Province ◽

Vegetation Coverage ◽

Slope Aspect ◽

Susceptibility Maps ◽

Debris Flow Susceptibility

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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