GIS-based landslide susceptibility mapping using ensemble methods for Fengjie County in the Three Gorges Reservoir Region, China

Gong, Wenping; Hu, Maosheng; Zhang, Y.; Tang, Huiming; Liu, D.; Song, Qiang

doi:10.1007/s13762-021-03572-z

Cited by 15 publications

(12 citation statements)

References 65 publications

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“…As discussed above, the relationship mapping between the environmental factors and the permafrost stability is fairly similar to that between the environmental factors and the landslide occurrence. It is noted that there are various approaches for mapping the relationship between the environmental factors and the landslide occurrence, such as the artificial neural network (Lee et al 2004;Gong et al, 2021), decision tree (Pradhan 2013), frequency ratio (Ozdemir and Altural 2013), and fuzzy assessment (Gemitzi et al, 2011;Gong et al, 2021). These methods can readily be adopted for mapping the relationship between the environmental factors and the permafrost stability.…”

Section: Random Forest Method-based Permafrost Stability Mappingmentioning

confidence: 99%

Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-series InSAR and Random Forest Method

Zhao

Gong

Ren

et al. 2022

Preprint

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Abstract. Ground deformation is an important index for evaluating the stability and degradation of the permafrost. Due to limited accessibility, in-situ measurement of the ground deformation of permafrost area on the Tibetan Plateau is a challenge. Thus, the technique of time-series Interferometric Synthetic Aperture Radar (InSAR) is often adopted for measuring the ground deformation of the permafrost area, the effectiveness of which is however degraded in the areas with geometric distortions in Synthetic Aperture Radar (SAR) images. In this study, a method that integrates InSAR and random forest method is proposed for an improved permafrost stability mapping on the Tibetan Plateau; and, to demonstrate the application of the proposed method, the permafrost stability mapping in a small area located in the central region of the Tibetan Plateau is studied. First, the ground deformation in the concerned area is studied with InSAR, in which 67 Sentinel-1 scenes taken in the period from 2014 to 2020 are collected and analyzed. Second, the relationship between the environmental factors (i.e., topography, land cover, land surface temperature, and distance-to-road) and the permafrost stability is mapped with the random forest method, based on the high-quality data extracted from initial InSAR analysis. Third, the permafrost stability in the areas where the visibility of SAR images is poor or the InSAR analysis results are not available is mapped with the trained random forest model. Comparative analyses demonstrate that the integration of InSAR and random forest method yields a more effective permafrost stability mapping, compared to the sole application of InSAR analysis.

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Section: Random Forest Method-based Permafrost Stability Mappingmentioning

confidence: 99%

Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-series InSAR and Random Forest Method

Zhao

Gong

Ren

et al. 2022

Preprint

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“…Researchers also have used the Ridge regression method for LCFs importance analysis [45]. Other popular methods for feature selection and analysis includes relative risk regression analyse [45], fractal analysis [48], resampling scheme analysis and Pearson's correlation analysis [49], correlation-based features selections (CFS) [50], frequency ratio (FR) [51], fuzzy and weights of LCFs using SVM [52], principal component analysis (PCA) to select independent and significant LCFs [53], information gain method [54], GeoDetector and recursive feature elimination (RFE) method for LCFs optimization to reduce redundancy [51], interactive detector [51], one rule (one-R) [42], correlation attributes evaluation (CAE) where greater calculated average merit (AM) indicates more influence of the LCF [55], sensitivity analysis [56], Spearman's rank correlation coefficient [57], relief-F method [58], Fischer score analysis [47], and gain ratio method [59].…”

Section: Legendmentioning

confidence: 99%

“…The weighted averaging employs an AUC-based weighted average to integrate the base classifiers. The qualitative matrix ensemble method [56] uses a decision matrix to combine two prediction outcomes to make a comprehensive decision. For example, one base classifier predicts a 'very high' susceptibility for a particular pixel while the other predicts a 'medium' susceptibility.…”

Section: Ensemble Techniquesmentioning

confidence: 99%

“…The susceptibility level will be the same if both base classifiers predict the same susceptibility level. The semi-quantitative partition [56] is an ensemble model where the region of study is partitioned into several sub-regions based on certain features such as topography in studies related to landslide susceptibility. The best prediction of all the subareas is combined to generate a new LSM.…”

Section: Ensemble Techniquesmentioning

confidence: 99%

“…The best prediction of all the subareas is combined to generate a new LSM. Probability-weighted [56] ensemble method assigns weights to prediction of different base-classifiers. The weights can be determined using certain features, such as the overall success rate.…”

Section: Ensemble Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

et al. 2022

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Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.

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A novel QLattice‐based whitening machine learning model of landslide susceptibility mapping

Sun,

Ding,

Wen

et al. 2023

Earth Surf Processes Landf

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Landslide susceptibility mapping (LSM) enables the prediction of landslide occurrences, thereby offering a scientific foundation for disaster prevention and control. In recent years, numerous studies have been conducted on LSM using machine learning techniques. However, the majority of machine learning models is considered “black box” models due to their lack of transparent explanations. In contrast, the QLattice model serves as a white box model, as it can elucidate the decision‐making mechanism while representing a novel approach to whitening machine learning models. QLattice possesses the capability to automatically select and scale data features. In this study, Fengjie County in China was selected as the research area, with slope units serving as evaluation units. A geospatial database was constructed using 12 conditioning factors, including elevation, slope, and annual average rainfall. LSM models were conducted using both the QLattice and random forest (RF) algorithms. The findings demonstrate that the QLattice model achieved an area under curve value of 0.868, while the RF model attained an area under curve value of 0.849 for the test datasets. These results highlight the superior predictive ability and stability of the QLattice model compared with RF. Furthermore, QLattice can explicate and clarify the change processes of conditioning factors, thereby revealing the internal decision‐making mechanism and causes behind the LSM model's decisions. The innovative QLattice‐based model provides new ideas and methodologies for LSM research.

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GIS-based landslide susceptibility mapping using ensemble methods for Fengjie County in the Three Gorges Reservoir Region, China

Cited by 15 publications

References 65 publications

Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-series InSAR and Random Forest Method

Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-series InSAR and Random Forest Method

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

A novel QLattice‐based whitening machine learning model of landslide susceptibility mapping

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