Application of a GIS-based slope unit method for landslide susceptibility mapping along the rapidly uplifting section of the upper Jinsha River, South-Western China

Sun, Xiaohui; Chen, Jianping; Han, Xudong; Bao, Yiding; Zhan, Jiewei; Peng, Wei

doi:10.1007/s10064-019-01572-5

Cited by 61 publications

(35 citation statements)

References 60 publications

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“…Improving the performance of models of LS susceptibility has still drawn enormous attention from the research community for proper mapping. The application of remote sensing and GIS can be useful in the study of hazard management due to their high accuracy and speed where accuracy and time are two vital aspects of landslide modeling [1,34,67,68].…”

Section: Discussionmentioning

confidence: 99%

“…This present study attempted to assess the proficiency of various methods such as knowledgedriven AHP, probabilistic FR and BLR models and ensemble models AHP-FR, AHP-BLR and FR-BLR to assess the LS susceptibility of Kurseong region of the Himalaya, India. The employ of ensemble methods to generate high accuracy mapping [68,69] is considered an important step in this regard. This Kurseong is chosen as a case study because of the high frequency of LS in eastern Himalaya and high sensitivity of this area in terms of population, concentration, tea garden and agricultural land.…”

Section: Discussionmentioning

confidence: 99%

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Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods

2020

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Kurseong municipality and its surrounding hill slope are famous high altitudinal residential areas in the Darjeeling Himalayan region of India. In Darjeeling Himalayas, huge landslides occur in the rainy season every year. This paper was aimed to delineate landslide susceptibility zone (LSZ) in Kurseong and its surrounding hill slope in Darjeeling Himalayas. Nine landslide inducing parameters (i.e., slope, altitude, rainfall, geological structure, distance from river channels, distance from lineament, soil type or characteristics, land-use/land-cover and aspect) were used for mapping LSZ applying analytic hierarchy process (AHP), frequency ratio (FR), binary logistic regression (BLR) models and their ensemble (AHP-FR; AHP-BLR; and FR-BLR) with the aid of ArcGIS, SPSS and ERDAS software. The landslide susceptible maps produced in GIS environment by the AHP, FR, BLR and their ensemble models were classified into five susceptibility classes such as very low, low, moderate, high and very high. The area under curve (AUC) of receiver operating characteristics (ROC) and kappa statistics were used to analyze the accuracy of the LSZ maps. The AUC values of prepared maps were 78.86% (AHP), 80.22% (FR), 80.67% (BLR), 83.44% (AHP-FR), 84.39% (AHP-BLR) and 84.73% (FR-BLR). Overall kappa statistics were 0.789 (AHP), 0.812 (FR), 0.799 (BLR), 0.837 (AHP-FR), 0856 (AHP-BLR) and 0.868 (FR-BLR), indicating the good accuracy of the models. The mapping of LSZ will be useful for land-use planning and future landslide hazard mitigation strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods

2020

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“…SU represents a slope or a part of a slope. Landslides are geological hazards that develop on slopes; the number of landslides that occur on a slope reflects the slope stability (Sun et al, 2020). Also, SU suppresses the bias introduced by the incomplete LIM (Reichenbach et al, 2018).…”

Section: Slope Unit Generationmentioning

confidence: 99%

A One-Class-Classifier-Based Negative Data Generation Method for Rapid Earthquake-Induced Landslide Susceptibility Mapping

Chen

Zhang

et al. 2021

Front. Earth Sci.

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Machine learning with extensively labeled training samples (e.g., positive and negative data) has received much attention in terms of addressing earthquake-induced landslide susceptibility mapping (LSM). However, the extensive amount of labeled training data required by machine learning, particularly the precise negative data (i.e., non-landslide area), cannot be easily and efficiently collected. To address this issue, this study presents a one-class-classifier-based negative data generation method for rapid earthquake-induced LSM. First, an incomplete landslide inventory (i.e., positive data) was produced with the aid of change detection using before-and-after satellite images and the Geographic Information System (GIS). Second, a one-class classifier was utilized to compute the probability of landslide occurrence based on the incomplete landslide inventory followed by the negative data generation from the low landslide susceptibility areas. Third, the positive data as well as the generated negative data (i.e., non-landslide) were compounded to train a traditional binary classifier to produce the final LSM. Experimental results suggest that the proposed method is capable of achieving a result that is comparable to methods using the complete landslide inventory, and it displays good correspondence with recent landslide events, making it a suitable method for rapid earthquake-induced LSM. The findings in this study would be useful in regional disaster planning and risk reduction.

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“…Landslide is a kind of natural hazard in the mountainous regions, threatening human life and property [1][2][3][4]. Over the past decades, the huge catastrophe ability of landslides attracted many researchers to devote themselves to assessing landslide susceptibilities [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…is method is more practical for landslide evaluation at the mesoscale and is the most widely used method in landslide sensitivity evaluation at present. Each evaluation model has been proved to have different advantages and disadvantages, some of which may be more suitable for solving specific problems in specific areas or implementing specific projects [2].…”

Section: Introductionmentioning

confidence: 99%

GIS‐Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China

Wang

Chen

2021

Advances in Civil Engineering

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Landslides are one of the nature hazards causing a lot of casualties and property losses in the world. Over the last decades, many researchers have made contributions in landslide susceptibility maps using qualitative and quantitative methods. Parameters of DEM, geology, etc. are selected to analyze the mechanism of landslides. The quality of data is essential in the landslide studies, and more credible results can be obtained if the data is adequate and accurate from the wide range of parameters. The aim of this study is to evaluate the landslide susceptibility of Huangyuan County of Qinghai. Through field investigations, 100 landslide disaster locations in the study area were selected, and 11 influencing factors including elevation, slope, aspect, plane curvature, profile curvature, road distance, river distance, fault distance, stratum rock property, vegetation coverage index, and terrain humidity index were selected as the influencing factors of landslide disaster based on GIS. In this paper, the information method (IM) model, frequency ratio (FR) model, and artificial neural network (ANN) model are used to evaluate the susceptibility of geological hazards, and the receiver operating characteristic (ROC) curve of disaster points at different levels is used to test the evaluation accuracy of three models. The results show that factors that have great influence on landslides are associated with witness, and the terrain humidity index has the highest weight in the occurrences of landslide. The values of AUC indicate that the ANN model is the best evaluation model suitable for the study area and can be extremely useful for landslide hazard mitigation strategies. Based on the calculation of ANN model, three valley areas are determined with high landslide susceptibility, and necessary reinforcement measures should be taken.

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Application of a GIS-based slope unit method for landslide susceptibility mapping along the rapidly uplifting section of the upper Jinsha River, South-Western China

Cited by 61 publications

References 60 publications

Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods

Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods

A One-Class-Classifier-Based Negative Data Generation Method for Rapid Earthquake-Induced Landslide Susceptibility Mapping

GIS‐Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China

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