Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.4
Journals
Water
Special Issues
Research on Landslide Hydrology and Hydrogeological Disaster Monitoring
share announcement

Research on Landslide Hydrology and Hydrogeological Disaster Monitoring

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

Deadline for manuscript submissions: closed (30 December 2022) | Viewed by 8249

Special Issue Editors

Prof. Dr. Wei Shao

E-Mail Website
Guest Editor
School of Hydrology and Water Resources, Nanjing University of Information Science & Technology, Nanjing, China
Interests: landslide hydrology; hydrometeorology
Prof. Dr. Wen Nie

E-Mail Website
Guest Editor
Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Science, Quanzhou 362200, China
Interests: landslide monitoring and early warning; smart mine; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junjun Ni

E-Mail Website
Guest Editor
School of Transportation, Southeast Universit, Nanjing 211189, China
Interests: ecological geotechnical engineering; unsaturated soil mechanics; resource utilization of engineering solid waste
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural and artificial slopes are widely distributed around the world and have a wide variety of types, including mountain slopes, coastal slopes, mine slopes, dikes, etc.  Hydrological extremes of unprecedented snowmelt and rainfall are the most common external causes of slope failures, and the subsequent long-runout landslides and debris flow cause severe mortality and economic loss. Assessment of landslide risk relies on the multidisciplinary monitoring of hydrological and geological conditions of potential landslides. The most advanced techniques in remote sensing and in situ monitoring bring more comprehensive information for in-depth analysis of slope failure and deformation mechanism, which can provide basis for early warning of landslide disaster.

This research theme aims to provide an outlet for peer-reviewed publications that combine multidisciplinary techniques in monitoring and modeling to conduct stability analysis and risk assessment of natural and engineering slopes. Topics include but are not limited to the following:

  • Hydrogeological monitoring of snowmelt/rainfall-induced landslides
  • Impact of soil bioengineering and vegetation on slope stability
  • Slope failure and deformation mechanisms under hydrological extremes of unprecedented snowmelt and rainfall
  • Monitoring and early warning of tailings dam failure and coastal landslide disaster
  • Numerical simulation of multiple-phase debris flow considering fluid-solid coupling
  • Landslides disaster prevention and mitigation techniques

Prof. Dr. Wei Shao
Prof. Dr. Wen Nie
Prof. Dr. Junjun Ni
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

  • landslide hydrology
  • rainfall-induced landslide
  • snowmelt-triggered landslide
  • numerical model
  • coastal landslide disaster
  • fluid-solid coupling
  • tailings dam landslide
  • soil bioengineering and vegetation

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

3 pages, 163 KiB  
Editorial
Research on Landslide Hydrology and Hydrogeological Disaster Monitoring
by Wei Shao, Wen Nie and Junjun Ni
Water 2023, 15(10), 1910; https://doi.org/10.3390/w15101910 - 18 May 2023
Viewed by 1071
Abstract
The triggering of landslides is strongly related to hydrological processes, as variations in soil moisture content and pore water pressure affect variations in soil mechanical properties [...] Full article
(This article belongs to the Special Issue Research on Landslide Hydrology and Hydrogeological Disaster Monitoring)

Research

Jump to: Editorial

17 pages, 3100 KiB  
Article
Landslide Displacement Prediction of Shuping Landslide Combining PSO and LSSVM Model
by Wenjun Jia, Tao Wen, Decheng Li, Wei Guo, Zhi Quan, Yihui Wang, Dexin Huang and Mingyi Hu
Water 2023, 15(4), 612; https://doi.org/10.3390/w15040612 - 04 Feb 2023
Cited by 8 | Viewed by 1795
Abstract
Predicting the deformation of landslides is significant for landslide early warning. Taking the Shuping landslide in the Three Gorges Reservoir area (TGRA) as a case, the displacement is decomposed into two components by a time series model (TSM). The least squares support vector [...] Read more.
Predicting the deformation of landslides is significant for landslide early warning. Taking the Shuping landslide in the Three Gorges Reservoir area (TGRA) as a case, the displacement is decomposed into two components by a time series model (TSM). The least squares support vector machine (LSSVM) model optimized by particle swarm optimization (PSO) is selected to predict the landslide displacement prediction based on rainfall and reservoir water level (RWL). Five parameters, including rainfall over the previous month, rainfall over the previous two months, RWL, change in RWL over the previous month and period displacement over the previous half year, are selected as the input variables. The relationships between the five parameters and the landslide displacement are revealed by grey correlation analysis. The PSO-LSSVM model is used to predict the periodic term displacement (PTD), and the least squares method is applied to predict the trend term displacement (TTD). With the same input variables, the back propagation (BP) model and the PSO-SVM model are also developed for comparative analysis. In the PSO-LSSVM model, the R2 of three monitoring stations is larger than 0.98, and the MAE values and the RMSE values are the smallest among the three models. The outcomes demonstrate that the PSO-LSSVM model has a high accuracy in predicting landslide displacement. Full article
(This article belongs to the Special Issue Research on Landslide Hydrology and Hydrogeological Disaster Monitoring)
►▼ Show Figures

Figure 1

16 pages, 3639 KiB  
Article
Inverse Estimation of Soil Hydraulic Parameters in a Landslide Deposit Based on a DE-MC Approach
by Sijie Chen, Haiwen Yan, Wei Shao, Wenjun Yu, Lingna Wei, Zongji Yang, Ye Su, Guangyuan Kan and Shaohui Luo
Water 2022, 14(22), 3693; https://doi.org/10.3390/w14223693 - 15 Nov 2022
Cited by 2 | Viewed by 1379
Abstract
Extreme rainfall is a common triggering factor of landslide disasters, for infiltration and pore water pressure propagation can reduce suction stress and shear strength at the slip surface. The subsurface hydrological model is an essential component in the early-warning system of rainfall-triggered landslides, [...] Read more.
Extreme rainfall is a common triggering factor of landslide disasters, for infiltration and pore water pressure propagation can reduce suction stress and shear strength at the slip surface. The subsurface hydrological model is an essential component in the early-warning system of rainfall-triggered landslides, whereas soil moisture and pore water pressure simulated by the Darcy–Richards equation could be significantly affected by uncertainties in soil hydraulic parameters. This study conducted an inverse analysis of in situ measured soil moisture in an earthquake-induced landslide deposit, and the soil hydraulic parameters were optimized with the Differential Evolution Markov chain Monte Carlo method (DE-MC). The DE-MC approach was initially validated with a synthetic numerical experiment to demonstrate its effectiveness in finding the true soil hydraulic parameters. Besides, the soil water characteristic curve (SWCC) and hydraulic conductivity function (HCF) described with optimized soil hydraulic parameter sets had similar shapes despite the fact that soil hydraulic parameters may be different. Such equifinality phenomenon in inversely estimated soil hydraulic parameters, however, did not affect the performance of simulated soil moisture dynamics in the synthetic numerical experiment. The application of DE-MC to a real case study of a landslide deposit also indicated satisfying model performance in terms of accurate match between the in situ measured soil moisture content and ensemble of simulations. In conclusion, based on the satisfying performance of simulated soil moisture and the posterior probability density function (PDF) of parameter sets, the DE-MC approach can significantly reduce uncertainties in specified prior soil hydraulic parameters. This study suggested the integration of the DE-MC approach with the Darcy–Richards equation for an accurate quantification of unsaturated soil hydrology, which can be an essential modeling strategy to support the early-warning of rainfall-triggered landslides. Full article
(This article belongs to the Special Issue Research on Landslide Hydrology and Hydrogeological Disaster Monitoring)
►▼ Show Figures

Figure 1

13 pages, 10948 KiB  
Article
Discrete Element Simulation of the Road Slope Considering Rainfall Infiltration
by Xiao Gu, Wen Nie, Qihang Li, Jiabo Geng, Tao Zhou and Canming Yuan
Water 2022, 14(22), 3663; https://doi.org/10.3390/w14223663 - 13 Nov 2022
Cited by 3 | Viewed by 1462
Abstract
Rainfall infiltration is the primary factor that induces landslides. In this paper, discrete element software (PFC3D) was used to establish a novel rainfall infiltration model, which integrates water transfer, intensity decay and seepage force into the calculation of the moisture field. [...] Read more.
Rainfall infiltration is the primary factor that induces landslides. In this paper, discrete element software (PFC3D) was used to establish a novel rainfall infiltration model, which integrates water transfer, intensity decay and seepage force into the calculation of the moisture field. By applying this model to the rainfall infiltration analysis of a road slope in Nanping City, Fujian Province, China, the distribution law of water content, the functional relationship between shear strength and water content, and the calculation of permeability at different times can be obtained. The model was verified by comparing simulated results of water content with field monitoring data. The simulation error of water content is lower than 10%. Furthermore, this model application was validated by reproducing the pressure variation of the retaining wall on 12 May 2022. To obtain the accuracy of this model application, it was compared with saturated water content model and seepage force model. The comparison results of the three models showed that the simulation results of this model are best matching with the observation data. Moreover, the verification and validation indicate that our proposed model can be used to effectively analyze the rainfall infiltration of road slope. Full article
(This article belongs to the Special Issue Research on Landslide Hydrology and Hydrogeological Disaster Monitoring)
►▼ Show Figures

Figure 1

15 pages, 6287 KiB  
Article
Stability Analysis of Embankment Slope Considering Water Absorption and Softening of Subgrade Expansive Soil
by Siyi Zhao, Jiantao Zheng and Jian Yang
Water 2022, 14(21), 3528; https://doi.org/10.3390/w14213528 - 03 Nov 2022
Cited by 6 | Viewed by 2513
Abstract
With the rapid development of road engineering today, a large number of high-grade highways need to pass through expansive soil distribution areas. At present, the research on expansive soil slope mainly focuses on the newly excavated cutting slope. However, according to engineering experience, [...] Read more.
With the rapid development of road engineering today, a large number of high-grade highways need to pass through expansive soil distribution areas. At present, the research on expansive soil slope mainly focuses on the newly excavated cutting slope. However, according to engineering experience, a landslide of fill embankment on expansive soil foundation is also very common. The expansive soil layer is heterogeneous. There are many weak intercalations or large fissures under the ground, which are generally parallel to the trend, with low strength and high permeability. After rainfall, the strength of the weak interlayer and large fissures will be further reduced after moisture absorption, and the sliding surface is easily formed under the load of filler, which is the main factor inducing embankment landslide. On the basis of landslide investigation and a laboratory test, a FORTRAN calculation program is developed in this paper, which can comprehensively consider the special moisture absorption and softening characteristics of expansive soil. Taking a high fill embankment slope with a soft interlayer in the Baoshan area of Yunnan Province as an example, the stability and instability characteristics of the fill slope on the expansive soil foundation are analyzed, and the influence of moisture absorption and softening on the expansive soil slope is emphatically discussed. Finally, this paper puts forward the reinforcement method of the high fill embankment slope on the soft expansive soil foundation, which is proven to have a good reinforcement effect through calculation analysis and field practice. For expansive soil foundation with weak interlayer, it is better to directly reinforce the weak layer through rigid piles. Full article
(This article belongs to the Special Issue Research on Landslide Hydrology and Hydrogeological Disaster Monitoring)
►▼ Show Figures

Figure 1

19 pages, 4940 KiB  
Article
A Tailings Dam Long-Term Deformation Prediction Method Based on Empirical Mode Decomposition and LSTM Model Combined with Attention Mechanism
by Yang Zhu, Yijun Gao, Zhenhao Wang, Guansen Cao, Renjie Wang, Song Lu, Wei Li, Wen Nie and Zhongrong Zhang
Water 2022, 14(8), 1229; https://doi.org/10.3390/w14081229 - 11 Apr 2022
Cited by 12 | Viewed by 2755
Abstract
Tailings dams are constructed as storage dams for ore waste, serving as industrial waste piles and for drainage. The dam is negatively affected by rainfall, infiltration lines and its own gravity, which can cause its instability to gradually increase, leading to dam deformation. [...] Read more.
Tailings dams are constructed as storage dams for ore waste, serving as industrial waste piles and for drainage. The dam is negatively affected by rainfall, infiltration lines and its own gravity, which can cause its instability to gradually increase, leading to dam deformation. To predict the irregular changes of tailings dam deformation, empirical mode decomposition (EMD) is applied to the deformation data to obtain the trend and periodic components. The attention mechanism is used to assign different weights to the input variables to overcome the limitation that the long short-term memory (LSTM) model can only generate fixed-length vectors. The lagged autocorrelation coefficient is applied to each decomposed subregion to solve the lagging effect of external factors on dam deformation. Finally, the model is used to predict deformation in multiple directions to test the generalization ability. The proposed method can effectively mitigate the problems of gradient disappearance and gradient explosion. The applied results show that, compared with the control model EMD-LSTM, the evaluation indexes RMSE and MAE improve 23.66% and 27.90%, respectively. The method also has a high prediction accuracy in the remaining directions of the tailings dam, which has a wide practical application effect and provides a new idea for tailings dam deformation mechanism research. Full article
(This article belongs to the Special Issue Research on Landslide Hydrology and Hydrogeological Disaster Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop