Ground fissure susceptibility mapping based on factor optimization and support vector machines

Wang, Xuebing; Zhang, Wengang; Chun-shan, Zhang; Tan, Chengxuan; Yan, Ping; Zhang, Zhihua; Guo, Jian

doi:10.1007/s10064-022-02843-4

Cited by 12 publications

(4 citation statements)

References 64 publications

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“…Huang et al (2022) considered the neighbourhood characteristics of landslide datasets and applied hydrological slope units in the LSM by using RF and support vector machine (SVM). Many other machine learning‐based models are also utilized in this field by different authors (Aleotti & Chowdhury, 1999; Chang et al, 2020; Guzzetti et al, 1999; Huang, Cao, Jiang, et al, 2020; Huang, Zhang, Zhou, et al, 2020; Jiang et al, 2018; Reichenbach et al, 2018; Wang, Wang, Zhang, Zhang, et al, 2022), indicating that machine learning based methods have been brought back into favour with scholars in assessing the complex relationships between landslide and the conditioning factors. Its capability to deal with a large amount of data makes it suitable for the classification tasks associated with landslide occurrence predictions.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

et al. 2023

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Landslide susceptibility mapping (LSM) has been widely used as an important reference for development and construction planning to mitigate the potential social‐eco impact caused by landslides. Originally, most of those maps were generated by the judgements of experts, which is time‐consuming and laborious, and whose accuracy is difficult to be quantified because of the subjective effects. With the development of machine learning algorithms and the methods of data collection, big data and artificial intelligence have now been popularized in this field, significantly improving mapping accuracy and efficiency. Various machine learning‐based methods, mainly including conventional machine learning, deep learning, and transfer learning have been applied and compared in LSM in different areas by previous researchers. Nevertheless, none of them can be effective in all cases. Although deep learning‐based methods were proven more accurate than conventional machine learning‐based methods in most data‐rich situations, the latter is sometimes more popularly used in LSM, as there is not that much data in this field to train a deep learning network perfectly. In a more rigorous situation when there is very limited data, transfer learning‐based approaches are applied by several researchers, which have contributed to improve the workability and the accuracy of LSM in data‐limited areas. Such technical explosion has promoted the application of landslide susceptibility maps, thus contributing to mitigating the social‐eco impact associated with landslides. This paper comprehensively reviews the whole process of generating landslide susceptibility maps based on machine learning methods, introduces and compares the commonly used machine learning methods, and discusses the topics for future research.

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Section: Introductionmentioning

confidence: 99%

A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

et al. 2023

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“…On the other hand, artificial intelligence techniques have been rapidly developed in recent years, resulting in machine learning (ML) becoming an advanced tool for solving complex geotechnical engineering problems (Atangana Njock et al, 2020; Du et al, 2021; Phoon & Zhang, 2022; X. Wang et al, 2022; W. Zhang, Gu, et al, 2022; W. Zhang, Li, et al, 2021; W. Zhang, Li, Han, et al, 2022; W. Zhang, Li, Tang, et al, 2022; W. Zhang, Li, Wu, et al, 2022; K. Zhang, Wang, et al, 2022; W. Zhang, Wu, et al, 2021). ML models are trained using historical datasets and can easily extract nonlinear relationships between input and output variables without too much prior information (Y. Chen, Xu, et al, 2022; J. Chen, Zhu, et al, 2022; W. Zhang, Gu, et al, 2022; W. Zhang, Li, Han, et al, 2022; W. Zhang, Li, Tang, et al, 2022; W. Zhang, Li, Wu, et al, 2022; K. Zhang, Wang, et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Dynamic assessment of slope stability based on multi‐source monitoring data and ensemble learning approaches: A case study of Jiuxianping landslide

Kang

Chen

et al. 2022

Geological Journal

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Accurate assessment of slope stability is the most important task in geological disaster prevention and control. This study developed an ensemble learning approach based on stacking strategy and eight commonly used machine learning (ML) models, for exploring the feasibility of the factor of safety (FS) prediction using dynamic multi‐source monitoring data of slopes and landslides. Based on long‐term and dynamic field monitoring and numerical calculation, a dataset for constructing the FS prediction model for the Jiuxianping landslide was established. The dataset includes five types of monitoring data namely rainfall, reservoir water level, groundwater level, surface displacement and deep displacement for a total of nine features, and one label FS. Four regularized regression models, kernel ridge regression (KRR), lasso, elastic net and support vector regression (SVR), as well as four ensemble learning models, random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), were adopted to obtain the nonlinear association between the nine features and the label FS, respectively. Based on five repeated 5‐fold cross‐validation (CV) and successive halving (SH) hyperparameter searching method, the hyperparameters of each model were determined, and the prediction effects of each optimal model were compared. The results show that the ensemble learning models outperform the common regression models. Furthermore, with the help of the stacking ensemble learning thinking, four excellent ensemble models were combined, and the final stacking ensemble learning model was used to predict the FS of the Jiuxianping landslide. The results indicate that the stacking model has better robustness and generalization performance. Besides, the feature relative importance of four ensemble learning models was analysed, for enhancing the interpretability of ML models and pointing out the research direction of feature engineering in the future.

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“…The development of rock mechanics and engineering involves transportation engineering, geological engineering, civil engineering, hydropower engineering and other professional fields of economic construction (Feng et al, 2019;He et al, 2021;Lawal and Kwon, 2021;Wu et al, 2021;Wang et al, 2022;Zhuang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Study on the deterioration trend of the rock mass on the reservoir banks under dry-wet cycles

Gao

Xiang²,

Xiang³

et al. 2022

Front. Ecol. Evol.

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The deterioration of rock mass under dynamic mechanical state is difficult to determine, especially when evolution trends of micro-fractures are considered. Taking the limestone from Wuxia section of the Three Gorges Reservoir area as studying case, cross-scale tests and theoretical analysis were adopted to study the influence of dry-wet cycles on rock deterioration. The weakly acidic condition (pH = 6) and the dry environment at 105°C were together constituted the two extreme mechanical states of the dry-wet cycles. Meanwhile, the number of dry-wet cycles and soaking time were used as the main variables to analyze the deterioration trend. After introducing the damage mechanics, the quantitative relationship between the mechanical parameters and changing mechanical states was obtained. Combined with the microstructure obtained by scanning electron microscope (SEM), it can be found that with the increase of dry-wet cycles and soaking time, the morphology of mineral particles changed, the number of secondary pores gradually increased, the surface structure became looser, and the microcracks gradually increased, causing the decrease of macro strength. The related method proposed in this paper could provide significant references to determine the mechanical parameters of rocky reservoir bank.

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Ground fissure susceptibility mapping based on factor optimization and support vector machines

Cited by 12 publications

References 64 publications

A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

Dynamic assessment of slope stability based on multi‐source monitoring data and ensemble learning approaches: A case study of Jiuxianping landslide

Study on the deterioration trend of the rock mass on the reservoir banks under dry-wet cycles

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