Landslide Susceptibility mapping using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing

Zhang, Wengang; He, Yuwei; Liu, Songlin; Meng, Xuanyu

doi:10.1002/gj.4683

Cited by 63 publications

(23 citation statements)

References 74 publications

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“…Assessing landslide susceptibility is crucial for managing landslide risks, but some analyses lack interpretability and have low accuracy (Can et al, 2021; Demir, 2019; Kavzoglu & Teke, 2022; Sun et al, 2021). Zhang, He, et al (2023) aim to address these deficiencies by evaluating the effectiveness of random forest (RF) and extreme gradient boosting (XGBoost) models in predicting landslide susceptibility in Fengjie County, a typical landslide‐prone area in southwest China. Field investigations yielded 1624 landslide occurrences from 1980 to 2020, and a geospatial database of 16 conditional factors was constructed.…”

Section: Research Outputs Of This Special Issuementioning

confidence: 99%

Application of artificial intelligence in geotechnical and geohazard investigations

et al. 2023

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The application of artificial intelligence (AI) and big data in geohazard investigations has gained popularity due to the development of machine learning algorithms and data collection methods. Previous studies have compared and applied various machine learning‐based methods, such as conventional machine learning, deep learning, and transfer learning in different areas. This special issue provides state‐of‐the‐art information on the use of AI in geotechnical research, particularly in the Three Gorges Reservoir (TGR) area and adjoining regions. The aim of this volume is to serve as a reference for future researchers interested in exploring the potential of AI in geohazard investigations. It is hoped that this special issue will contribute to the development of guidelines for enhancing the application of AI and big data in geotechnical research, thereby improving our understanding of geological terrains and their associated hazards.

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Section: Research Outputs Of This Special Issuementioning

confidence: 99%

Application of artificial intelligence in geotechnical and geohazard investigations

et al. 2023

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“…Du et al and Jia et al further conducted experimental research on dumping perilous rock and found that the natural vibration frequency is closely related to the stability [30][31][32][33]. However, the above research has just started and is in the experimental stage, and the theory has not yet been perfected [34,35]. And the above test found that the dynamic characteristic parameters of the perilous rock are closely related to the stability [36].…”

Section: Introductionmentioning

confidence: 99%

Stability Evaluation of Sliding-Type Perilous Rock in Huangzangsi Hydrojunction Project Based on Natural Vibration Frequency

et al. 2023

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The instability of perilous rock is mostly manifested as sudden collapse and failure without obvious displacement characteristics. Therefore, it is difficult to achieve the purpose of monitoring and early warning by conventional displacement monitoring. But the existing stability monitoring indicators are mostly deformation, stress, and strain. There is a problem that the stability evaluation parameters are inconsistent with the monitoring parameters. Taking sliding-type perilous rock as the research object, the structural plane is assumed to be homogeneous and isotropic, and linear elastic deformation in the amplitude range. Based on the dynamic theory and limit equilibrium model, the quantitative relationship model can be established involving safety factors, natural vibration frequency, structural surface bonding area, elastic modulus, and mass. The remote laser vibrometer is used to monitor the natural vibration frequency of the sliding-type perilous rock on the slope of the Huangzangsi Hydrojunction, and the stability evaluation of the perilous rock is achieved based on the quantitative relationship model between the safety factor and the natural vibration frequency. In this way, the frequency of slipping perilous rock stability evaluation and safety factor can be monitored. The results are basically the same with the safety factor calculated by the limit equilibrium method, indicating that the method is correct and feasible. The research has high theoretical significance and practical value for the safety monitoring and advanced warning of sliding perilous rock.

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“…Landslide deformation is mainly caused by a variety of factors. The complex process of landslide deformation makes it difficult to accurately distinguish the stages of landslide deformation [18][19][20][21]. Therefore, the traditional empirical means are no longer applicable.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Landslide Displacement Based on the Variational Mode Decomposition and GWO-SVR Model

Wang

Wei

2023

Sustainability

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Accurate prediction of landslide displacement is an effective way to reduce the risk of landslide disaster. Under the influence of periodic precipitation and reservoir water level, many landslides in the Three Gorges Reservoir area underwent significant displacement deformation, showing a similar step-like deformation curve. Given the nonlinear characteristics of landslide displacement, a prediction model is established in this study according to the variational mode decomposition (VMD) and support vector regression (SVR) optimized by gray wolf optimizer (GWO-SVR). First, the original data are decomposed into trend, periodic and random components by VMD. Then, appropriate influential factors are selected using the grey relational degree analysis (GRDA) method for constructing the input training data set. Finally, the sum of the three displacement components is superimposed as the total displacement of the landslide, and the feasibility of the model is subsequently tested. Taking the Shuizhuyuan landslide in the Three Gorges Reservoir area as an example, the accuracy of the model is verified using the long time-series monitoring data. The results indicate that the newly proposed model achieves a relatively good prediction accuracy with data decomposition and parameter optimization. Therefore, this model can be used for the predict the accuracy of names and affiliations ion of landslide displacement in the Three Gorges Reservoir area.

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Landslide Susceptibility mapping using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing

Cited by 63 publications

References 74 publications

Application of artificial intelligence in geotechnical and geohazard investigations

Application of artificial intelligence in geotechnical and geohazard investigations

Stability Evaluation of Sliding-Type Perilous Rock in Huangzangsi Hydrojunction Project Based on Natural Vibration Frequency

Prediction of Landslide Displacement Based on the Variational Mode Decomposition and GWO-SVR Model

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