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Risk Analysis in Landslides and Groundwater-Related Hazards

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

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 14979

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Prof. Dr. Wei Chen

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College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
Interests: hydrogeology; environmental impact assessment; natural hazard susceptibility; spatial modeling; machine learning; geology; civil engineering
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Dr. Paraskevas Tsangaratos

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Department of Geological Studies, School of Mining and Metallurgical Engineering, National University of Athens, 15780 Athens, Greece
Interests: natural hazards; water resources; engineering geology; GIS; machine learning; soft computing; remote sensing
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Dr. Ioanna Ilia

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Department of Geological Studies, School of Mining and Metallurgical Engineering, National University of Athens, 15780 Athens, Greece
Interests: natural hazards; environmental geology; engineering geology; GIS; machine learning
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Dr. Xia Zhao

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Guest Editor

Special Issue Information

Dear Colleagues,

Many disasters related to global climate change and water, such as landslides, groundwater, and floods, occur all over the world each year. In most cases, natural disasters of this kind have caused serious financial and human losses worldwide. It is mainly caused by the gradual or extreme action of factors related to the climate, structure, geological morphology process, and human activities that have a negative impact on the geological environment. Although the scientific community tries to simulate these phenomena with high accuracy to obtain the risk of landslide- and groundwater-related hazards, some characteristics leading to their evolution and occurrence are still unclear. Natural disasters seem to be complex in nature, as are the changes in frequency, speed, duration, and affected areas. All these characteristics make it a rather difficult task to fully understand the mechanism behind its evolution and occurrence.

Accurate and timely prediction of these disasters and identification of their risks can not only protect people from injury and death but also reduce property losses and economic losses caused by these disasters. Advances in science and technology have greatly improved our disaster management capabilities. However, it is still necessary to apply advanced prediction tools to various landslide- and groundwater-related disasters to analyze their risks.

This Special Issue aims to provide an outlet for peer-reviewed publications that implement state-of-the-art methods and techniques incorporating spatial analysis, AI, and ML methods to map, monitor, evaluate, and assess landslide and groundwater disasters, emphasize our understanding of disaster mechanisms, and build a safer future. This Special Issue focuses on the risks related to landslide and groundwater hazards and invites contributions using the most advanced research as well as case studies and lessons learned from failure, including but not limited to:

Sequential landslide monitoring, earthquake landslide, landslide caused by rainfall, geotechnical engineering problems related to landslide, landslide risk prediction and assessment, landslide triggering and failure mechanism, numerical modeling and GIS application zoning of hazards, the development of new monitoring techniques and forecasting models for early warning systems, etc.
Mechanism of groundwater related disasters, numerical analysis method of rock soil fluid solid coupling, groundwater evolution law, spatial isotope data and model, groundwater seismic effect model, groundwater risk assessment and dynamic control, water resources assessment and management, groundwater dating and paleohydrology, new trends and challenges of isotope hydrology, etc.

Prof. Dr. Wei Chen
Dr. Paraskevas Tsangaratos
Dr. Ioanna Ilia
Dr. Xia Zhao
Guest Editors

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Keywords

landslide susceptibility
landslide hazard analysis
risk analysis
risk evaluation
risk management
rainfall
groundwater
modelling
monitoring

Published Papers (8 papers)

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Research

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15 pages, 4446 KiB

Open AccessArticle

Integrating Microseismic Monitoring for Predicting Water Inrush Hazards in Coal Mines

by Huiqing Lian, Qing Zhang, Shangxian Yin, Tao Yan, Hui Yao, Songlin Yang, Jia Kang, Xiangxue Xia, Qixing Li, Yakun Huang, Zhengrui Ren, Wei Wu and Baotong Xu

Water 2024, 16(8), 1168; https://0-doi-org.brum.beds.ac.uk/10.3390/w16081168 - 20 Apr 2024

Viewed by 351

Abstract

The essence of roof water inrush in coal mines fundamentally stems from the development of water-bearing fracture zones, facilitating the intrusion of overlying aquifers and thereby leading to water hazard incidents. Monitoring rock-fracturing conditions through the analysis of microseismic data can, to a certain extent, facilitate the prediction and early warning of water hazards. The water inflow volume stands as the most characteristic type of data in mine water inrush accidents. Hence, we investigated the feasibility of predicting water inrush events through anomalies in microseismic data from the perspective of water inflow volume variations. The data collected from the microseismic monitoring system at the 208 working face were utilized to compute localization information and source parameters. Based on the hydrogeological conditions of the working face, the energy screening range and its calculation grid characteristics were determined, followed by the generation of kernel density cloud maps at different depths. By observing these microseismic kernel density cloud maps, probabilities of roof water-conducting channel formation and potential locations were inferred. Subsequently, based on the positions of these roof water-conducting channels on the planar domain, the extension depth and expansion direction of the water-conducting channels were determined. Utilizing microseismic monitoring data, a quantitative assessment of water inrush risk was conducted, thereby establishing a linkage between microseismic data and water (inrush) data, which are two indirectly related datasets. The height of microseismic events was directly proportional to the trend of water inflow in the working face. In contrast, the occurrence of water inflow events and microseismic events exhibited a specific lag effect, with microseismic events occurring prior to water inrush events. Abnormalities in microseismic monitoring data partially reflect changes in water-conducting channel patterns. When connected with coal seam damage zones, water inrush hazards may occur. Therefore, abnormalities in microseismic monitoring data can be regarded as one of the precursor signals indicating potential floor water inrushes in coal seams. Full article

(This article belongs to the Special Issue Risk Analysis in Landslides and Groundwater-Related Hazards)

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29 pages, 6086 KiB

Open AccessArticle

Debris Flow Risk Assessment for the Large-Scale Temporary Work Site of Railways—A Case Study of Jinjia Gully, Tianquan County

by Yunpu Wu, Yu Lei and Haihua Gu

Water 2024, 16(8), 1152; https://0-doi-org.brum.beds.ac.uk/10.3390/w16081152 - 18 Apr 2024

Viewed by 451

Abstract

Temporary works are necessary to ensure the construction and operation of railways. These works are characterized by their large scale, numerous locations, and long construction periods. However, suitable land resources for such purposes are extremely limited in mountainous railway areas. Additionally, the selection of sites for these works often overlaps with areas affected by debris flow, leading to high potential risks from geological disasters. Taking the Jinjia Gully watershed as an example, this paper explores a method for assessing debris flow risks in single gullies, including the zoning of debris flow danger areas, vulnerability analysis, and risk assessment. Based on the data obtained from field surveys, they utilize ArcGIS and the Analytic Hierarchy Process (AHP), combined with numerical simulations and indoor experiments, to establish a quantitative risk assessment method for large-scale temporary works. The results indicate that (1) the area of debris flow hazard zones decreases with increasing rainfall frequency, and (2) the vulnerability assessment model can not only reflect the types of individual work, structural materials, and construction quality but also the shielding effect of building clusters. In the direction of flow, the shielding effect range of buildings on debris flow accumulation fans is approximately 37.5 times the size of the buildings. In the direction of extension, when the angle between current and rear buildings exceeds 0.674 radians, the shielding effect can be neglected. (3) At a rainfall frequency of p = 5%, more than 80% of large-scale temporary works are in extremely low or low-risk zones, indicating that the study area is at a low risk level. Full article

(This article belongs to the Special Issue Risk Analysis in Landslides and Groundwater-Related Hazards)

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15 pages, 29514 KiB

Open AccessArticle

Integrated Geotechnical and Electrical Resistivity Tomography to Map the Lithological Variability Involved and Breaking Surface Evolution in Landslide Context: A Case Study of the Targa Ouzemour (Béjaia)

by Hallal Nassim, Lamali Atmane, Hamai Lamine, Hamidatou Mouloud and Mazari Anes

Water 2024, 16(5), 682; https://0-doi-org.brum.beds.ac.uk/10.3390/w16050682 - 26 Feb 2024

Cited by 1 | Viewed by 703

Abstract

The specific lithology of the southern part of Bejaia city represents a major limitation to urban settlement and expansion. This is partly due to landslides that tend to affect this region. To date, one of these landslides in this region has occurred in the Targa Ouzemour area, where the damage extended approximately six hectares. The main purpose of this study is to identify the failure surfaces characterizing the internal structure of this landslide as well as the significant influence of groundwater on slope instability, which manifests as surface cracking and subsidence. We have combined several geotechnical and geophysical methods, including field observations. The exploitation of the collected geotechnical data from the six (06) boreholes drilled in the landslide zone has allowed for knowledge to be gained on the lithological components, as well as the characterizations of physical and mechanical properties on a range of different types of affected rocks, whereas electrical resistivity tomography (ERT) data allowed an in-depth examination, leading us to reconstruct the landslide geometry and particularly to evaluate the hydrological characteristics of the studied site. Moreover, the resistivity contrast patterns provided more clarity to discern between the various lithological formations that are still stable or actively moving within this landslide. All these findings have contributed to the construction of a characteristic geomodel that highlights the failure surfaces over which displacement is still experienced. Finally, with the evidence of rainfall effects on the deformation and stability of the slope, specific landslide remedial measures were accordingly suggested. Full article

(This article belongs to the Special Issue Risk Analysis in Landslides and Groundwater-Related Hazards)

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25 pages, 5101 KiB

Open AccessArticle

Land Subsidence Due to Groundwater Exploitation in Unconfined Aquifers: Experimental and Numerical Assessment with Computational Fluid Dynamics

by Dayana Carolina Chalá, Edgar Quiñones-Bolaños and Mehrab Mehrvar

Water 2024, 16(3), 467; https://0-doi-org.brum.beds.ac.uk/10.3390/w16030467 - 31 Jan 2024

Cited by 1 | Viewed by 983

Abstract

Land subsidence is a global challenge that enhances the vulnerability of aquifers where climate change and driving forces are occurring simultaneously. To comprehensively analyze this issue, integrated modeling tools are essential. This study advances the simulation of subsidence using Computational Fluid Dynamics (CFD); it assessed the effects of exploitation and recharge of groundwater on the vertical displacement of coarse and fine sands in a laboratory-scale aquifer. A model was developed by integrating the Navier–Stokes equations to study the groundwater flow and Terzaghi’s law for the vertical displacement of sands. The boundary conditions used were Dirichlet based on the changes in the hydraulic head over time. The specific storage coefficient was used to calibrate the model. The findings confirmed that subsidence occurs at slower rates in soil with fine sands with average particle diameters of 0.39 mm than in coarse sands with average particle diameters of 0.67 mm. The maximum discrepancy between the experimental and the numerical reaffirms that CFD platforms can be used to simulate subsidence dynamics and potentially allow the simultaneous simulation of other dynamics. Concluding remarks and recommendations are highlighted considering the up-to-date advances and future work to improve the research on subsidence in unconfined aquifers. Full article

(This article belongs to the Special Issue Risk Analysis in Landslides and Groundwater-Related Hazards)

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15 pages, 7242 KiB

Open AccessArticle

Identification Method of River Blocking by Debris Flow in the Middle Reaches of the Dadu River, Southwest of China

by Zhi Song, Gang Fan, Yanni Chen and Darui Liu

Water 2023, 15(24), 4301; https://0-doi-org.brum.beds.ac.uk/10.3390/w15244301 - 18 Dec 2023

Viewed by 665

Abstract

Debris flow is a typical natural disaster in the middle reaches of the Dadu River, which seriously threatens the safety of life and property of local residents. However, there is currently a lack of a comprehensive analysis methods applicable to the blockage of river channels by debris flow in the Dadu River basin, limiting disaster prevention and mitigation in this area. Based on previous large-scale model tests carried out in the middle reaches of the Dadu River, the debris flows are divided into dam-type debris flows and submerged debris flows. The calculation formulas for the maximum travel distance of the two kinds of debris flows entering the river are obtained via theoretical derivation. The formulas for calculating the length and volume of debris flow accumulation are derived, and the relationship between the debris flow loss coefficient and river blocking degree in the middle part of the Dadu River is analyzed. An identification method of river blocking by debris flow is put forward in this study. By calculating the maximum blocking degree, S (the ratio of the maximum driving distance of the debris flow to the width of the river), and the volume of the source materials needed to form a debris flow dam under the conditions that the debris flow does not reach the opposite bank (V₁), reaches the opposite bank but does not block the river (V₂), and reaches the opposite bank (V₃), the form of debris flow blocking the river is distinguished. When S = 1, V > V₃, complete blockage occurs; when S = 1, V > V₂, the river is mostly blocked; when S < 1, V > V₁, the river is half-blocked. This study established an identification method of river blocking by debris flow, providing a basis for early warning for river blocking and disaster prevention in the middle reaches of the Dadu River. Full article

(This article belongs to the Special Issue Risk Analysis in Landslides and Groundwater-Related Hazards)

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21 pages, 4387 KiB

Open AccessArticle

Comparative Analysis of Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping: A Case Study in Rize, Turkey

by Ayse Yavuz Ozalp, Halil Akinci and Mustafa Zeybek

Water 2023, 15(14), 2661; https://0-doi-org.brum.beds.ac.uk/10.3390/w15142661 - 22 Jul 2023

Cited by 10 | Viewed by 2258

Abstract

The Eastern Black Sea Region is regarded as the most prone to landslides in Turkey due to its geological, geographical, and climatic characteristics. Landslides in this region inflict both fatalities and significant economic damage. The main objective of this study was to create landslide susceptibility maps (LSMs) using tree-based ensemble learning algorithms for the Ardeşen and Fındıklı districts of Rize Province, which is the second-most-prone province in terms of landslides within the Eastern Black Sea Region, after Trabzon. In the study, Random Forest (RF), Gradient Boosting Machine (GBM), CatBoost, and Extreme Gradient Boosting (XGBoost) were used as tree-based machine learning algorithms. Thus, comparing the prediction performances of these algorithms was established as the second aim of the study. For this purpose, 14 conditioning factors were used to create LMSs. The conditioning factors are: lithology, altitude, land cover, aspect, slope, slope length and steepness factor (LS-factor), plan and profile curvatures, tree cover density, topographic position index, topographic wetness index, distance to drainage, distance to roads, and distance to faults. The total data set, which includes landslide and non-landslide pixels, was split into two parts: training data set (70%) and validation data set (30%). The area under the receiver operating characteristic curve (AUC-ROC) method was used to evaluate the prediction performances of the models. The AUC values showed that the CatBoost (AUC = 0.988) had the highest prediction performance, followed by XGBoost (AUC = 0.987), RF (AUC = 0.985), and GBM (ACU = 0.975) algorithms. Although the AUC values of the models were close to each other, the CatBoost performed slightly better than the other models. These results showed that especially CatBoost and XGBoost models can be used to reduce landslide damages in the study area. Full article

(This article belongs to the Special Issue Risk Analysis in Landslides and Groundwater-Related Hazards)

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28 pages, 7203 KiB

Open AccessArticle

Discriminant Analysis of Water Inrush Sources in the Weibei Coalfield, Shaanxi Province, China

by Weifeng Xue, Enke Hou, Xia Zhao, Yong Ye, Paraskevas Tsangaratos, Ioanna Ilia and Wei Chen

Water 2023, 15(3), 453; https://0-doi-org.brum.beds.ac.uk/10.3390/w15030453 - 23 Jan 2023

Cited by 1 | Viewed by 1494

Abstract

Water inrush disasters in mining areas are one of the most serious geological disasters in coal mining. The purpose of this study is to study the establishment of a water chemical database and water inrush source discrimination model in the Weibei coalfield to provide the basis for regional hydrogeological conditions for future mining under pressure in the Weibei area, as well as a basis for the rapid identification of water inrush sources in the Weibei coalfield. In this paper, a conventional hydrochemical and trace element discrimination model for mine water inrush was established, and the hydrochemical characteristic files of the entire mining area were integrated. Based on 10 indicators, three hydrochemical discrimination models of rock stratum aquifers were established. Through the Mahalanobis distance test, it was found that the six selected variables, K⁺ + Na⁺, Mg²⁺, NH₄⁺, Cl⁻, SO₄²⁻, and pH, have significant discrimination ability and good effect and can effectively distinguish the three main water inrush aquifers in the Weibei mining area. Then, the clustering stepwise discriminant analysis method was used to select 24 water samples and 14 trace element indicators from the conventional water chemistry test results. Based on principal component analysis, a principal component analysis discriminant model of trace elements was established for the four main aquifers. The accuracy and misjudgment rate of the Bayes multi-class linear discriminant using conventional ions as explanatory variables were 64.3% and 35.7%, respectively, showing a poor discriminant effect. On this basis, seven characteristic trace elements were analyzed according to Bayes multi-class linear discriminant analysis, the mutual influence and restriction relationship regarding the migration of these seven trace elements in the groundwater system of the mining area was determined, and the modified Bayes multi-class linear discriminant analysis model of trace elements for the water inrush source was established, which was more accurate than the conventional ion Bayes multi-class linear discriminant analysis model. The accuracy rate reached 92.9%. This research is of great significance for mine water-source identification and water-inrush prevention guidance. Full article

(This article belongs to the Special Issue Risk Analysis in Landslides and Groundwater-Related Hazards)

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Review

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29 pages, 6841 KiB

Open AccessReview

Selected Worldwide Cases of Land Subsidence Due to Groundwater Withdrawal

by Ploutarchos Tzampoglou, Ioanna Ilia, Konstantinos Karalis, Paraskevas Tsangaratos, Xia Zhao and Wei Chen

Water 2023, 15(6), 1094; https://0-doi-org.brum.beds.ac.uk/10.3390/w15061094 - 13 Mar 2023

Cited by 3 | Viewed by 6947

Abstract

The present review paper focuses on selected cases around the world of land subsidence phenomena caused by the overexploitation of aquifers. Land subsidence is closely related to human activity. In particular, the development of technology has led to an exponential increase in industrial and agricultural production, as well as extensive urbanization, mainly in large cities. The action of those parameters, along with the effects of climate change, has led to further increases in water demands, which have mainly been served by overexploitation of the aquifers. Overexploitation, in conjunction with broader geo-tectonic conditions, can trigger severe land subsidence phenomena, resulting in significant damage affecting the physical and man-made environment. The scope of the present study is to provide a critical review of the existing literature on land subsidence due to aquifer overexploitation and highlight the main causal factors driving this process. The methods developed in the past and their outcomes hold significant importance in sustainable development strategic planning. Full article

(This article belongs to the Special Issue Risk Analysis in Landslides and Groundwater-Related Hazards)

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