Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: A comparative study from the loess plateau area in Shanxi (China)

Tang, Yaming; Fan, Feng; Guo, Zhengfu; Feng, Wei; Li, Zhengguo; Wang, Jiayun; Sun, Qiaoyin; Ma, Hongna; Li, Yane

doi:10.1016/j.jclepro.2020.124159

Cited by 89 publications

(51 citation statements)

References 76 publications

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“…SLM performed better in terms of accuracy due to having a determined target. The application of machine learning or deep learning has verified their advancement [51,52]. However, high accuracy plays the most important role in LSM but should not be the only consideration.…”

Section: Comparison Of Unsupervised and Supervised Learning For Lsmmentioning

confidence: 99%

A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

et al. 2021

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Landslides cause huge damage to social economy and human beings every year. Landslide susceptibility mapping (LSM) occupies an important position in land use and risk management. This study is to investigate a hybrid model which makes full use of the advantage of supervised learning model (SLM) and unsupervised learning model (ULM). Firstly, ten continuous variables were used to develop a ULM which consisted of factor analysis (FA) and k-means cluster for a preliminary landslide susceptibility map. Secondly, 351 landslides with “1” label were collected and the same number of non-landslide samples with “0” label were selected from the very low susceptibility area in the preliminary map, constituting a new priori condition for a SLM, and thirteen factors were used for the modeling of gradient boosting decision tree (GBDT) which represented for SLM. Finally, the performance of different models was verified using related indexes. The results showed that the performance of the pretreated GBDT model was improved with sensitivity, specificity, accuracy and the area under the curve (AUC) values of 88.60%, 92.59%, 90.60% and 0.976, respectively. It can be concluded that a pretreated model with strong robustness can be constructed by increasing the purity of samples.

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Section: Comparison Of Unsupervised and Supervised Learning For Lsmmentioning

confidence: 99%

A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

et al. 2021

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“…Based on geological data, the area is characterized by Cambrian to Jurassic sedimentary rocks and quaternary deposits. Sandstone, mudstone, sandy mudstone, and quaternary loess strata outcrop extensively (Tang et al, 2020). The area has a warm temperate continental monsoon climate with long cold winters and hot summers.…”

Section: Study Areamentioning

confidence: 99%

“…Landslide inventory map can reveal the spatial distribution of landslides and is a necessary means to analyze the relationship between the landslide points and inducing factors (Tian et al, 2019). The study area is a landslide-prone area, which suffered many landslide hazards with various scales in history (Wang et al, 2019;Tang et al, 2020). To obtain the updated information of landslides in the area, several filed surveys were conducted during 2016 and 2018.…”

Section: Landslide Inventory Mappingmentioning

confidence: 99%

“…Finally, there are total 466 landslides in the area revealed by the landslide inventory map, among which 234 are loess landslides and 232 are rockfalls (Tang et al, 2020). Given that the mechanisms of the two types of landslides are totally different, this study only deals with the issue associated with loess landslides.…”

Section: Landslide Inventory Mappingmentioning

confidence: 99%

“…However, the performance of landslide susceptibility models is normally data-dependent, which means not only causal factors but also other data (e.g., the data availability and resolution) can affect the results (Catani et al, 2013). Hence, a causal factor may have different effects on landslides in different test occasions as the geological background of every area is unique (Tang et al, 2020). In view of this, several controversial but common factors in the literature were still selected in this paper.…”

Section: Landslide Causal Factorsmentioning

confidence: 99%

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Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China

et al. 2021

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Integration of different models may improve the performance of landslide susceptibility assessment, but few studies have tested it. The present study aims at exploring the way to integrating different models and comparing the results among integrated and individual models. Our objective is to answer this question: Will the integrated model have higher accuracy compared with individual model? The Lvliang mountains area, a landslide-prone area in China, was taken as the study area, and ten factors were considered in the influencing factors system. Three basic machine learning models (the back propagation (BP), support vector machine (SVM), and random forest (RF) models) were integrated by an objective function where the weight coefficients among different models were computed by the gray wolf optimization (GWO) algorithm. 80 and 20% of the landslide data were randomly selected as the training and testing samples, respectively, and different landslide susceptibility maps were generated based on the GIS platform. The results illustrated that the accuracy expressed by the area under the receiver operating characteristic curve (AUC) of the BP-SVM-RF integrated model was the highest (0.7898), which was better than that of the BP (0.6929), SVM (0.6582), RF (0.7258), BP-SVM (0.7360), BP-RF (0.7569), and SVM-RF models (0.7298). The experimental results authenticated the effectiveness of the BP-SVM-RF method, which can be a reliable model for the regional landslide susceptibility assessment of the study area. Moreover, the proposed procedure can be a good option to integrate different models to seek an “optimal” result.

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Refinement analysis of landslide risk assessment for wide area based on UAV-acquired high spatial resolution images

Mao,

Yu,

et al. 2024

Stoch Environ Res Risk Assess

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Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: A comparative study from the loess plateau area in Shanxi (China)

Cited by 89 publications

References 76 publications

A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China

Refinement analysis of landslide risk assessment for wide area based on UAV-acquired high spatial resolution images

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