Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs

Xu, Jiancong; Jiang, Yu; Yang, Chunli

doi:10.3390/app12126056

Cited by 17 publications

(8 citation statements)

References 39 publications

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“…When processing sequence data, a RNN will update the internal state based on the current input and the previous moment's state and produce the output. A RNN model can further improve the accuracy of landslide displacement prediction [75,76]. However, RNNs have two main shortcomings: errors often accumulate during the prediction process, and the position of attention is not always accurate.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

Cheng,

Wang,

Huang

et al. 2024

Remote Sensing

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Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future.

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Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

Cheng,

Wang,

Huang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…However, there are still few studies on tunnel reliability, only relatively concentrated on the above aspects [20]. It is significant to note that the subject of structural reliability analysis in civil engineering has recently been further developed [21][22][23]. To incorporate extra information into the Gaussian process as constraints, a Bayesian-entropy Gaussian process methodology for regression and surrogate modeling was proposed [21].…”

Section: Introductionmentioning

confidence: 99%

“…XGBoost does not require high-performance hardware resources such as memory, and has strong robustness. Compared to deep learning models, XGBoost can achieve similar results without fine-tuning of parameters [23]. The surrogate modeling technology based on the XGBoost algorithm has recently been applied to the real-time design of tunnel alignment [24].…”

Section: Introductionmentioning

confidence: 99%

NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel

Xu,

Sun,

Rui

2024

Applied Sciences

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How to evaluate the reliability of deep soft rock tunnels under high stress is a very important problem to be solved. In this paper, we proposed a practical stochastic reliability method based on the third-generation non-dominated sorting genetic algorithm (NSGA–III) and eXtreme Gradient Boosting (XGBoost). The proposed method used the Latin hypercube sampling method to generate the dataset samples of geo-mechanical parameters and adopted XGBoost to establish the model of the nonlinear relationship between displacements and surrounding rock mechanical parameters. And NSGA–III was used to optimize the surrogate model hyper-parameters. Finally, the failure probability was computed by the optimized surrogate model. The proposed approach was firstly implemented in the analysis of a horseshoe-shaped highway tunnel to illustrate the efficiency of the approach. Then, in comparison to the support vector regression method and the back propagation neural network method, the feasibility, validity and advantages of XGBoost were demonstrated for practical problems. Using XGBoost to achieve Monte Carlo simulation, a surrogate solution can be provided for numerical simulation analysis to overcome the time-consuming reliability evaluation of initial support structures in soft rock tunnels. The proposed method can evaluate quickly the large deformation disaster risks of non-circular deep soft rock tunnels.

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“…Besides, the eXtreme Gradient Boosting (XGBoost) algorithm does not require high hardware resources such as memory and has strong robustness. Compared to deep learning models, XGBoost can achieve similar results without fine-tuning the parameters (Xu et al, 2022). The surrogate modelling technology based on the XGBoost algorithm has recently been applied to the real-time design of tunnel alignment (Yan et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Adam - Bayesian - Gaussian process regression based Monte Carlo simulation reliability analysis of deep soft rock tunnel

Xu,

Yang,

Rui

2024

Preprint

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To evaluate the reliability of deep soft rock tunnels is a very important issue to be solved. In this paper, we proposed a novel Monte Carlo simulation reliability analysis method (MCS-RAM) integrating adaptive momentum stochastic optimization algorithm (Adam), Bayesian inference theory and Gaussian process regression (GPR) ——ABGPR-MCS-RAM, and implemented it by Python. The proposed method used the Latin hypercube sampling method to generate the dataset sample of geo-mechanical parameters, constructed combined kernel functions of GPR and adopted GPR to establish the surrogate model of the nonlinear mapping relationship between displacements and mechanical parameters of the surrounding rock. Adam was used to optimize the hyper-parameters of the surrogate model. Bayesian inference algorithm was used to obtain the probability distribution of geotechnical parameters and the optimal surrounding rock mechanical parameters. Finally, the failure probability was computed by the MCS-RAM based on the optimized surrogate model. Through the application of engineering case, the results indicate that the proposed method has less prediction error and stronger prediction ability than Kriging or XGBoost, and it can significantly save computational time compared with the traditional polynomial response surface method (PRSM). The proposed method can be used in the reliability analysis of all shapes of tunnels.

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Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs

Cited by 17 publications

References 39 publications

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel

Adam - Bayesian - Gaussian process regression based Monte Carlo simulation reliability analysis of deep soft rock tunnel

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