Risk Analysis in Landslides and Groundwater-Related Hazards

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

Deadline for manuscript submissions: 20 March 2024 | Viewed by 15021

Special Issue Editors

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

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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30 pages, 10299 KiB  
Article
Hybrid Integration of Bagging and Decision Tree Algorithms for Landslide Susceptibility Mapping
Water 2024, 16(5), 657; https://doi.org/10.3390/w16050657 - 23 Feb 2024
Viewed by 191
Abstract
Landslides represent a significant global natural hazard, threatening human settlements and the natural environment. The primary objective of the study was to develop a landslide susceptibility modeling approach that enhances prediction accuracy and informs land-use planning decisions. The study utilized a hybrid ensemble-based [...] Read more.
Landslides represent a significant global natural hazard, threatening human settlements and the natural environment. The primary objective of the study was to develop a landslide susceptibility modeling approach that enhances prediction accuracy and informs land-use planning decisions. The study utilized a hybrid ensemble-based methodology to improve prediction accuracy and effectively capture the complexity of landslide susceptibility patterns. This approach harnessed the power of ensemble models, employing a bagging algorithm with base learners, including the reduced error pruning decision tree (REPTree) and functional tree (FT) models. Ensemble models are particularly valuable because they combine the strengths of multiple models, enhancing the overall performance and robustness of the landslide susceptibility prediction. The study focused on Yanchuan County, situated within the hilly and gully region of China’s Loess Plateau, known for its susceptibility to landslides, using sixteen critical landslide conditioning factors, encompassing topographic, environmental, and geospatial variables, namely elevation, slope, aspect, proximity to rivers and roads, rainfall, the normalized difference vegetation index, soil composition, land use, and more. Model performances were evaluated and verified using a range of metrics, including receiver operating characteristic (ROC) curves, trade-off statistical metrics, and chi-square analysis. The results demonstrated the superiority of the integrated models, particularly the bagging FT (BFT) model, in accurately predicting landslide susceptibility, as evidenced by its high area under the curve area (AUC) value (0.895), compared to the other models. The model excelled in both positive predictive rate (0.847) and negative predictive rate (0.886), indicating its efficacy in identifying landslide and non-landslide areas and also in the F-score metric with a value of 0.869. The study contributes to the field of landslide risk assessment, offering a significant investigation tool for managing and mitigating landslide hazards in Yanchuan County and similar regions worldwide. 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  
Article
Land Subsidence Due to Groundwater Exploitation in Unconfined Aquifers: Experimental and Numerical Assessment with Computational Fluid Dynamics
Water 2024, 16(3), 467; https://doi.org/10.3390/w16030467 - 31 Jan 2024
Viewed by 603
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); [...] Read more.
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  
Article
Identification Method of River Blocking by Debris Flow in the Middle Reaches of the Dadu River, Southwest of China
Water 2023, 15(24), 4301; https://doi.org/10.3390/w15244301 - 18 Dec 2023
Viewed by 567
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 [...] Read more.
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  
Article
Comparative Analysis of Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping: A Case Study in Rize, Turkey
Water 2023, 15(14), 2661; https://doi.org/10.3390/w15142661 - 22 Jul 2023
Cited by 6 | Viewed by 1981
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 [...] Read more.
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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21 pages, 8161 KiB  
Article
Optimizing Rotation Forest-Based Decision Tree Algorithms for Groundwater Potential Mapping
Water 2023, 15(12), 2287; https://doi.org/10.3390/w15122287 - 19 Jun 2023
Cited by 1 | Viewed by 1082
Abstract
Groundwater potential mapping is an important prerequisite for evaluating the exploitation, utilization, and recharge of groundwater. The study uses BFT (best-first decision tree classifier), CART (classification and regression tree), FT (functional trees), EBF (evidential belief function) benchmark models, and RF-BFTree, RF-CART, and RF-FT [...] Read more.
Groundwater potential mapping is an important prerequisite for evaluating the exploitation, utilization, and recharge of groundwater. The study uses BFT (best-first decision tree classifier), CART (classification and regression tree), FT (functional trees), EBF (evidential belief function) benchmark models, and RF-BFTree, RF-CART, and RF-FT ensemble models to map the groundwater potential of Wuqi County, China. Firstly, select sixteen groundwater spring-related variables, such as altitude, plan curvature, profile curvature, curvature, slope angle, slope aspect, stream power index, topographic wetness index, stream sediment transport index, normalized difference vegetation index, land use, soil, lithology, distance to roads, distance to rivers, and rainfall, and make a correlation analysis of these sixteen groundwater spring-related variables. Secondly, optimize the parameters of the seven models and select the optimal parameters for groundwater modeling in Wuqi County. The predictive performance of each model was evaluated by estimating the area under the receiver operating characteristic (ROC) curve (AUC) and statistical index (accuracy, sensitivity, and specificity). The results show that the seven models have good predictive capabilities, and the ensemble model has a larger AUC value. Among them, the RF-BFT model has the highest success rate (AUC = 0.911), followed by RF-FT (0.898), RF-CART (0.894), FT (0.852), EBF (0.824), CART (0.801), and BFtree (0.784), respectively. Groundwater potential maps of these 7 models were obtained, and four different classification methods (geometric interval, natural breaks, quantile, and equal interval) were used to reclassify the obtained GPM into 5 categories: very low (VLC), low (LC), moderate (MC), high (HC), and very high (VHC). The results show that the natural breaks method has the best classification performance, and the RF-BFT model is the most reliable. The study highlights that the proposed ensemble model has more efficient and accurate performance for groundwater potential mapping. Full article
(This article belongs to the Special Issue Risk Analysis in Landslides and Groundwater-Related Hazards)
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23 pages, 15021 KiB  
Article
Credal-Decision-Tree-Based Ensembles for Spatial Prediction of Landslides
Water 2023, 15(3), 605; https://doi.org/10.3390/w15030605 - 03 Feb 2023
Cited by 4 | Viewed by 2433
Abstract
Spatial landslide susceptibility assessment is a fundamental part of landslide risk management and land-use planning. The main objective of this study is to apply the Credal Decision Tree (CDT), adaptive boosting Credal Decision Tree (AdaCDT), and random subspace Credal Decision Tree (RSCDT) models [...] Read more.
Spatial landslide susceptibility assessment is a fundamental part of landslide risk management and land-use planning. The main objective of this study is to apply the Credal Decision Tree (CDT), adaptive boosting Credal Decision Tree (AdaCDT), and random subspace Credal Decision Tree (RSCDT) models to construct landslide susceptibility maps in Zhashui County, China. The observed 169 historical landslides were classified into two groups: 70% (118 landslides) for training and 30% (51 landslides) for validation. To compare and validate the performance of the three models, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) were utilized. Specifically, the success rates of the CDT model, AdaCDT model, and RSCDT model were 0.788, 0.821, and 0.847, respectively, while the corresponding prediction rates were 0.771, 0.802, and 0.861, respectively. In sum, the two ensemble models can effectively improve the performance accuracy of an individual CDT model, and the RSCDT model was proven to be superior to the other two models. Therefore, ensemble models are capable of being novel and promising approaches for the spatial prediction and zonation of a certain region’s landslide susceptibility. 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  
Article
Discriminant Analysis of Water Inrush Sources in the Weibei Coalfield, Shaanxi Province, China
Water 2023, 15(3), 453; https://doi.org/10.3390/w15030453 - 23 Jan 2023
Cited by 1 | Viewed by 1382
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 [...] Read more.
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  
Review
Selected Worldwide Cases of Land Subsidence Due to Groundwater Withdrawal
Water 2023, 15(6), 1094; https://doi.org/10.3390/w15061094 - 13 Mar 2023
Cited by 3 | Viewed by 5846
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 [...] Read more.
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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