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Rainfall-Induced Landslides and Natural Geohazards
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Rainfall-Induced Landslides and Natural Geohazards

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrogeology".

Deadline for manuscript submissions: closed (20 August 2024) | Viewed by 8868

Special Issue Editors

Dr. Linwei Li

E-Mail Website
Guest Editor
College of Resources and Environmental Engineering, Guizhou University, Guiyang, China
Interests: hydrodynamic landslide; landslide prediction; slope stability; machine learning; multi-source data mining in geosciences
Dr. Fasheng Miao

E-Mail Website
Guest Editor
Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
Interests: geohazards; prediction; risk assessment; remote sensing; landslides
Special Issues, Collections and Topics in MDPI journals
Dr. Wenmin Yao

E-Mail Website
Guest Editor
School of Civil Engineering, Zhengzhou University, Zhengzhou, China
Interests: landslide; stabilizing pile; water-rock interaction; geotechnical uncertainty; cyclic wetting-drying action; rock mechanics; multi-scale structures of geomaterials

Special Issue Information

Dear Colleagues,

Rainfall is the main trigger factor for various natural geohazards, such as landslides, rock avalanches, debris flows, and ground collapses. In recent years, with the frequent occurrence of extreme rainfall events, the natural geohazards have correspondingly increased around the world. This not only constantly poses a huge threat to human life and property, but also seriously damages the balance of natural ecosystems. Thus, it is of great significance to explore the mechanisms, evaluation methods, and prediction models of rainfall-induced landslides and natural geohazards for disaster risk management and ecological environment protection.

This Special Issue invites the submission of original research papers covering the latest findings and progress in the field of rainfall-induced landslides and natural geohazards. The topics of interest include but are not limited to:

  • Mechanism analysis of rainfall-induced natural geohazards using physical or data-driven methods.
  • Numerical modeling and stability analysis of natural geohazards under extreme rainfall conditions.
  • Spatial/temporal prediction models for natural geohazards considering extreme rainfall events.

Dr. Linwei Li
Dr. Fasheng Miao
Dr. Wenmin Yao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • rainfall-induced landslide
  • rainfall-induced natural geohazards
  • numerical modeling
  • mechanism analysis
  • stability analysis
  • spatial prediction
  • temporal prediction
  • data-driven method

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Published Papers (5 papers)

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Research

37 pages, 17961 KiB  
Article
Physical Model Experiments and Numerical Simulation Study on the Formation Mechanisms of Landslides on Gently Inclined Loess–Bedrock Contact Surfaces—A Case Study of the Libi Landslide in Shanxi Province
by Peng Li, Chenyang Wu, Haibo Jiang, Qingbo Chen, Huanxu Chen, Wei Sun and Huiwei Luo
Water 2024, 16(22), 3267; https://doi.org/10.3390/w16223267 - 14 Nov 2024
Cited by 1 | Viewed by 1313
Abstract
Landslides on gently inclined loess–bedrock contact surfaces are common geological hazards in the northwestern Loess Plateau region of China and pose a serious threat to the lives and property of local residents as well as sustainable regional development. Taking the Libi landslide in [...] Read more.
Landslides on gently inclined loess–bedrock contact surfaces are common geological hazards in the northwestern Loess Plateau region of China and pose a serious threat to the lives and property of local residents as well as sustainable regional development. Taking the Libi landslide in Shanxi Province as a case study (with dimensions of 400 m × 340 m, maximum thickness of 35.0 m, and volume of approximately 3.79 × 104 m3, where the slip zone is located within the highly weathered sandy mudstone layer of the Upper Shihezi Formation of the Permian System), this study employed a combination of physical model experiments and numerical simulations to thoroughly investigate the formation mechanism of gently inclined loess landslides. Via the use of physical model experiments, a landslide model was constructed at a 1:120 geometric similarity ratio in addition to three scenarios: rainfall only, rainfall + rapid groundwater level rise, and rainfall + slow groundwater level rise. The dynamic changes in the water content, pore water pressure, and soil pressure within the slope were systematically monitored. Numerical simulations were conducted via GEO-STUDIO 2012 software to further verify and supplement the physical model experimental results. The research findings revealed that (1) under rainfall conditions alone, the landslide primarily exhibited surface saturation and localized instability, with a maximum displacement of only 0.028 m, which did not lead to overall instability; (2) under the combined effects of rainfall and rapid groundwater level rise, a “sudden translational failure mode” developed, characterized by rapid slope saturation, abrupt stress adjustment, and sudden overall instability; and (3) under conditions of rainfall and a gradual groundwater level rise, a “progressive translational failure mode” emerged, experiencing four stages: initiation, development, acceleration, and activation, ultimately resulting in translational sliding of the entire mass. Through a comparative analysis of physical model experiments, numerical simulation results, and field monitoring data, it was verified that the Libi landslide belongs to the “progressive translational failure mode”, providing important theoretical basis for the identification, early warning, and prevention of such types of landslides. Full article
(This article belongs to the Special Issue Rainfall-Induced Landslides and Natural Geohazards)
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19 pages, 3656 KiB  
Article
Integrating Feature Selection with Machine Learning for Accurate Reservoir Landslide Displacement Prediction
by Qi Ge, Jingyong Wang, Cheng Liu, Xiaohong Wang, Yiyan Deng and Jin Li
Water 2024, 16(15), 2152; https://doi.org/10.3390/w16152152 - 30 Jul 2024
Cited by 6 | Viewed by 1513
Abstract
Accurate prediction of reservoir landslide displacements is crucial for early warning and hazard prevention. Current machine learning (ML) paradigms for predicting landslide displacement demonstrate superior performance, while often relying on various feature engineering techniques, such as decomposing into different temporal lags and feature [...] Read more.
Accurate prediction of reservoir landslide displacements is crucial for early warning and hazard prevention. Current machine learning (ML) paradigms for predicting landslide displacement demonstrate superior performance, while often relying on various feature engineering techniques, such as decomposing into different temporal lags and feature selection. This study investigates the impact of various feature selection techniques on the performance of ML algorithms for landslide displacement prediction. The Shuping and Baishuihe landslides in China’s Three Gorges Reservoir Area are used to comprehensively benchmark four prevalent ML algorithms. Both static ML models, including backpropagation neural network (BPNN), support vector machine (SVM), and dynamic models, such as long short-term memory (LSTM), and gated recurrent unit (GRU), are included. Each ML model is evaluated under three feature engineering techniques: raw multivariate time series, and feature selection under maximal information coefficient-partial autocorrelation function (MIC-PACF), or grey relational analysis-PACF (GRA-PACF). The results demonstrate that appropriate feature selection methods could significantly improve the performance of static ML models. In contrast, dynamic models effectively leverage inherent capabilities in capturing temporal dynamics within raw multivariate time series, seeing marginal gains with extensive feature engineering compared to no feature selection strategy. The optimal feature selection approach varies based on the ML model and specific landslide, highlighting the importance of case-specific assessments. The findings in this study offer guidance on integrating feature selection techniques with different machine learning models to maximize the robustness and generalizability of data-driven landslide displacement prediction frameworks. Full article
(This article belongs to the Special Issue Rainfall-Induced Landslides and Natural Geohazards)
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20 pages, 8832 KiB  
Article
Displacement Prediction Method for Rainfall-Induced Landslide Using Improved Completely Adaptive Noise Ensemble Empirical Mode Decomposition, Singular Spectrum Analysis, and Long Short-Term Memory on Time Series Data
by Ke Yang, Yi Wang and Gonghao Duan
Water 2024, 16(15), 2111; https://doi.org/10.3390/w16152111 - 26 Jul 2024
Cited by 1 | Viewed by 1421
Abstract
Landslide disasters frequently result in significant casualties and property losses, underscoring the critical importance of research on landslide displacement prediction. This paper introduces an approach combining improved empirical mode decomposition (ICEEMDAN) and singular entropy-enhanced singular spectrum analysis (SSA) to predict landslide displacement using [...] Read more.
Landslide disasters frequently result in significant casualties and property losses, underscoring the critical importance of research on landslide displacement prediction. This paper introduces an approach combining improved empirical mode decomposition (ICEEMDAN) and singular entropy-enhanced singular spectrum analysis (SSA) to predict landslide displacement using a time series short-duration memory network (LSTM). Initially, ICEEMDAN decomposes the landslide displacement time series into trend and periodic terms. SSA is then employed to denoise these components before fitting the trend term with LSTM. Pearson correlation analysis is utilized to identify characteristic factors within the LSTM model, followed by predictions using a multivariate LSTM model. The empirical results from the Baijiabao landslide in the Three Gorges Reservoir area demonstrate that the joint ICEEMDAN-SSA approach, when combined with LSTM modeling, outperforms the separate applications of SSA and ICEEMDAN, as well as other models such as RNN and SVM. Specifically, the ICEEMDAN-SSA-LSTM model achieves an RMSE of 6.472 mm and an MAE of 4.992 mm, which are considerably lower than those of the RNN model (19.945 mm and 15.343 mm, respectively) and the SVM model (16.584 mm and 11.748 mm, respectively). Additionally, the R2 value for the ICEEMDAN-SSA-LSTM model is 97.5%, significantly higher than the RNN model’s 72.3% and the SVM model’s 92.8%. By summing the predictions of the trend and periodic terms, the cumulative displacement prediction is obtained, indicating the superior accuracy of the ICEEMDAN-SSA-LSTM model. This model provides a new benchmark for precise landslide displacement prediction and contributes valuable insights to related research. Full article
(This article belongs to the Special Issue Rainfall-Induced Landslides and Natural Geohazards)
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13 pages, 6586 KiB  
Article
An Inversion Study of Reservoir Colluvial Landslide Permeability Coefficient by Combining Physical Model and Data-Driven Models
by Xiaopeng Yue, Yankun Wang and Tao Wen
Water 2024, 16(5), 686; https://doi.org/10.3390/w16050686 - 26 Feb 2024
Cited by 3 | Viewed by 1348
Abstract
The saturated permeability coefficient (ks) is a key parameter for evaluating the seepage and stability of reservoir colluvial landslides. However, ks values obtained from traditional experimental methods are often characterized by large variations and low representativeness. As a result, there are [...] Read more.
The saturated permeability coefficient (ks) is a key parameter for evaluating the seepage and stability of reservoir colluvial landslides. However, ks values obtained from traditional experimental methods are often characterized by large variations and low representativeness. As a result, there are significant deviations from actual observations when used in seepage field calculations for reservoir landslide analysis. This study proposes an intelligent inversion method that combines a physical model and a data-driven model for reservoir landslide ks based on actual groundwater level (GWL) monitoring data. This method combines Latin Hypercube Sampling (LHS), unsaturated flow finite element (FE) analysis, particle swarm optimization algorithm (PSO), and kernel extreme learning machine model (KELM). Taking the Hongyanzi landslide in Sichuan Province, China, as the research object, the GWL of the landslide under different ks was first obtained by LHS and transient seepage FE analysis. Then, a nonlinear functional relationship between ks and the landslide GWL was fitted based on the PSO-KELM model. Finally, the optimal landslide ks was obtained by minimizing the root-mean-squared error between the predicted and actual GWL using the PSO. A global sensitivity analysis was also conducted on the ks of different rock and soil layers to reveal their control rules on the calculation of landslide GWL. The research results demonstrate the feasibility of the proposed method and provide valuable information for similar landslides in practice. Full article
(This article belongs to the Special Issue Rainfall-Induced Landslides and Natural Geohazards)
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14 pages, 3222 KiB  
Article
Modeling Rainfall Impact on Slope Stability: Computational Insights into Displacement and Stress Dynamics
by Jingmei Zong, Changjun Zhang, Leifei Liu and Lulu Liu
Water 2024, 16(4), 554; https://doi.org/10.3390/w16040554 - 11 Feb 2024
Cited by 4 | Viewed by 2283
Abstract
The susceptibility of loess slopes to collapses, landslides, and sinkholes is a global concern. Rainfall is a key factor exacerbating these issues and affecting slope stability. In regions experiencing significant infrastructure and urban growth, understanding and mitigating rainfall effects on loess landslides is [...] Read more.
The susceptibility of loess slopes to collapses, landslides, and sinkholes is a global concern. Rainfall is a key factor exacerbating these issues and affecting slope stability. In regions experiencing significant infrastructure and urban growth, understanding and mitigating rainfall effects on loess landslides is crucial. ADINA numerical software 9 was utilized to explore rain-induced erosion’s influence on landslide dynamics. The simulations were based on local rainfall trends. The rainfall intensities examined were as follows: 200 mm/day, 300 mm/day, and 400 mm/day. The results indicate a pronounced impact of rainfall intensity on both the movement and stress levels within the slope. Higher rainfall intensities lead to increased movement and a wider stress impact area at the base of the slope. It was observed that surface movement is minimal at the slope crest but increases towards the bottom, with the greatest movement seen at the slope’s base. Full article
(This article belongs to the Special Issue Rainfall-Induced Landslides and Natural Geohazards)
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