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

Dou, Qi; Qin, Shengwu; Zhang, Yichen; Ma, Zhongjun; Chen, Junjun; Qiao, Shuangshuang; Hu, Xinyu; Liu, Fei

doi:10.3390/e21070695

Cited by 14 publications

(2 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unfortunately, there is not presently an accepted benchmark for the selection of debris flow influencing factors. Therefore, based on previous researches and experts experience [40], a total of thirteen influencing factors were selected, including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), distance to roads, distance to rivers, lithology, population density, annual rainfall, topography, and vegetation coverage. The digital elevation model (DEM) was downloaded from Google Maps with a spatial resolution of 100 m. To improve the quality of DFSM, grid cells were used in this paper which are recognized by the majority of researchers.…”

Section: Influencing Factorsmentioning

confidence: 99%

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

Chen

Qin

Qiao

et al. 2020

Water

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Influencing Factorsmentioning

confidence: 99%

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

Chen

Qin

Qiao

et al. 2020

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…[28] mapped debris flow susceptibility based on different machine learning methods, using topography, vegetation, human activity, and soil factors. Dou et al (2019) [33] proposed a method for the improved determination of controlling factors for debris flow susceptibility mapping, based on information fusion. Di et al (2019) [29] assessed debris flow susceptibility in southwest China using a Gradient Boosting machine learning method.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Evaluation of Assessment Models for Landslide Susceptibility Mapping in Permafrost Areas

Wang,

Shan,

Guo

et al. 2024

Progress in Landslide Research and Technology

View full text Add to dashboard Cite

China's permafrost regions are affected by global warming and the thawing of permafrost, and the occurrence of landslide disasters has become more and more frequent, which makes the evaluation of the susceptibility to geologic hazards in China's permafrost regions is an urgent work to be carried out. Most of the existing geohazard susceptibility models have fewer test cases in the permafrost regions. Twelve evaluation factors, such as altitude, slope, slope direction, land use, and lithology, were selected to draw landslide hazard susceptibility maps by using three commonly used landslide susceptibility assessment models, including the information value model, the frequency ratio model and the random forest model, which can be implemented in GIS, taking the Lesser Khingan Mountains area located in the eastern part of permafrost region of northeast China as the study area. The applicability of the above commonly used landslide susceptibility assessment models in the permafrost regions is carried out by fieldwork and comparing the results of and model simulation. The Random Forest Model was also used to assess the importance of the factors that were adopted and to judge the degree of their influence on landslide development. The results show that the Information Value Model has a better applicability in the permafrost region. However, due to factors such as climate warming and permafrost degradation, the accuracy of the prediction results obtained by applying the existing commonly used landslide susceptibility assessment models in permafrost regions are still in need to be improved. Finally, thawing and degradation of permafrost will play a non-negligible role in influencing the occurrence of landslides in permafrost regions.

show abstract

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

Cited by 14 publications

References 57 publications

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

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

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

Evaluation of Assessment Models for Landslide Susceptibility Mapping in Permafrost Areas

Contact Info

Product

Resources

About