Editorial

4 pages, 170 KiB

Open AccessEditorial

Overview of Comprehensive Risk Assessment Methods and Hazards Early Warning System for Geological Hazards in the Mountain Area

by Chun Zhu, Yingze Xu, Zhigang Tao, Hong-Hu Zhu, Chen Cao and Manchao He

Remote Sens. 2023, 15(9), 2239; https://doi.org/10.3390/rs15092239 - 23 Apr 2023

Viewed by 1167

Abstract

Many major projects are under construction in the mountain and surrounding areas [...] Full article

(This article belongs to the Special Issue Comprehensive Risk Assessment Methods and Hazards Early Warning System of Geological Hazards in the Mountain Area)

Research

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17 pages, 2299 KiB

Open AccessArticle

A Research on Cross-Regional Debris Flow Susceptibility Mapping Based on Transfer Learning

by Ruiyuan Gao, Changming Wang, Songling Han, Hailiang Liu, Xiaoyang Liu and Di Wu

Remote Sens. 2022, 14(19), 4829; https://doi.org/10.3390/rs14194829 - 27 Sep 2022

Cited by 5 | Viewed by 1449

Abstract

Debris flow susceptibility mapping (DFSM), which has proven to be one of the most effective tools for risk management, faces a variety of problems. To realize the rational use of debris flow sample resources and improve the modeling efficiency, a unified model based [...] Read more.

Debris flow susceptibility mapping (DFSM), which has proven to be one of the most effective tools for risk management, faces a variety of problems. To realize the rational use of debris flow sample resources and improve the modeling efficiency, a unified model based on transfer learning was established for cross-regional DFSM. First, samples with 10 features collected from two debris flow-prone areas were separately used to perform factor prediction ability analysis (FPAA) based on the information gain ratio (IGR) method and then develop traditional machine learning models based on random forests (RF). Secondly, two feature matrices representing different areas were projected into a common latent feature space to obtain two new feature matrices. Then, the samples with new features were used together for FPAA and developing a unified machine learning model. Finally, the performance of the models was obtained and compared based on the area under curves (AUC) and some statistical results. All the conditioning factors played different roles in debris flow prediction in the two study areas, based on which two traditional models and a unified model were established. The unified model based on feature transferring realized efficient cross-regional modeling, solved the unconvincing problem of limited sample modeling, and enabled more accurate identification of some debris flow samples. Full article

(This article belongs to the Special Issue Comprehensive Risk Assessment Methods and Hazards Early Warning System of Geological Hazards in the Mountain Area)

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28 pages, 11952 KiB

Open AccessArticle

Rockfall Hazard Assessment in the Taihang Grand Canyon Scenic Area Integrating Regional-Scale Identification of Potential Rockfall Sources

by Jiewei Zhan, Zhaoyue Yu, Yan Lv, Jianbing Peng, Shengyuan Song and Zhaowei Yao

Remote Sens. 2022, 14(13), 3021; https://doi.org/10.3390/rs14133021 - 23 Jun 2022

Cited by 9 | Viewed by 1977

Abstract

Frequent rockfall events pose a major threat to the safe operation of the Taihang Grand Canyon Scenic Area (GCSA) in China. The traditional techniques for identifying potential rockfall sources and hazard assessment methods are often challenged in the alpine canyon landform. This study [...] Read more.

Frequent rockfall events pose a major threat to the safe operation of the Taihang Grand Canyon Scenic Area (GCSA) in China. The traditional techniques for identifying potential rockfall sources and hazard assessment methods are often challenged in the alpine canyon landform. This study aims to establish an early identification framework for regional potential rockfall sources applicable to the canyon region and to assess rockfall hazards in potentially hazardous areas using unmanned aerial vehicle (UAV) photogrammetry. Specifically, by incorporating high-precision topographic information and geotechnical properties, the slope angle distribution method was used for static identification of potential rockfall sources. Moreover, SBAS-InSAR technology was used to describe the activity of potential rockfall sources. Finally, taking the key potentially hazardous area of the Sky City scenic spot as an example, the Rockfall Analyst tool was used to analyze the rockfall frequency, bounce height and energy characteristics based on the high-precision UAV 3D real scene model, and the analytic hierarchy process was introduced to achieve quantitative rockfall hazard assessment. The results show that the potential rockfall source areas in the Taihang GCSA is 33.47 km² (21.47%), mainly distributed in strips on the cliffs on both sides of the canyon, of which the active rockfall source area is 2.96 km² (8.84%). Taking the scenic spot of Sky City as example, the proposed UAV-based real scene modeling technology was proven to be able to quickly and accurately construct a 3D high-precision model of the canyon area. Moreover, the 3D rockfall simulation showed that the high-energy rockfall area was mainly distributed at the foot of the steep cliff, which mainly threatens the tourist distribution center below. The early identification and quantitative evaluation scheme of rockfall events proposed in this study can provide technical reference for the prevention and control of rockfall hazards in similar alpine valley areas. Full article

(This article belongs to the Special Issue Comprehensive Risk Assessment Methods and Hazards Early Warning System of Geological Hazards in the Mountain Area)

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23 pages, 8592 KiB

Open AccessArticle

Probabilistic Evaluation of Slope Reliability Considering Groundwater Level Uncertainty Based on Dynamic Agent Model Using Uniform Design

by Qing Ling, Qin Zhang, Wei Qu and Jing Zhang

Remote Sens. 2022, 14(12), 2779; https://doi.org/10.3390/rs14122779 - 09 Jun 2022

Cited by 1 | Viewed by 1448

Abstract

Due to the adverse influence of landslide disasters on human lives, property, and infrastructures, slope reliability analysis has attracted worldwide attention. However, many problems such as the neglect of the uncertainty in the water table level and the balance between the performance and [...] Read more.

Due to the adverse influence of landslide disasters on human lives, property, and infrastructures, slope reliability analysis has attracted worldwide attention. However, many problems such as the neglect of the uncertainty in the water table level and the balance between the performance and efficiency in conventional models are still unresolved. This study investigates the influence of the uncertainty in the water table level on the benefit of considering such uncertainty in slope reliability analysis. For this purpose, a new method, i.e., a dynamic whale optimization algorithm (WOA)–Gaussian process regression (GPR) agent model using uniform design with the consideration of uncertainty in the groundwater level, is proposed for slope probabilistic analysis in this paper. Then the developed technique is integrated with Monte Carlo Simulation (MCS) to obtain the slope failure probability. The benefit of the proposed method is illustrated through two practical landslides. The results demonstrate that the developed technique has better performance, as compared to MCS, the v-support vector machine (v-SVR), and the generalized regression neural network (GRNN). This may be attributed to the dynamic updating of the training samples provided by the uniform design, the optimal hyper-parameters optimized by WOA, or the GPR model that has strong generalization ability with limited samples. Furthermore, a small failure probability is obtained without considering the groundwater level uncertainty, which offers an optimistic estimate of landslide stability. Therefore, it is necessary to consider the probabilistic features of the groundwater level, especially for complicated landslides in high mountainous areas where the location of the water table level is not accurately available due to their inaccessibility to people and instruments. Full article

(This article belongs to the Special Issue Comprehensive Risk Assessment Methods and Hazards Early Warning System of Geological Hazards in the Mountain Area)

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16 pages, 7429 KiB

Open AccessArticle

Research on Generalized RQD of Rock Mass Based on 3D Slope Model Established by Digital Close-Range Photogrammetry

by Qing Ding, Fengyan Wang, Jianping Chen, Mingchang Wang and Xuqing Zhang

Remote Sens. 2022, 14(9), 2275; https://doi.org/10.3390/rs14092275 - 09 May 2022

Cited by 6 | Viewed by 1798

Abstract

The traditional method of obtaining rock quality designation (RQD) cannot fully reflect the anisotropy of the rock mass and thus cannot accurately reflect its quality. In the method of calculating RQD based on three-dimensional network simulation of discontinuities, due to the limited number [...] Read more.

The traditional method of obtaining rock quality designation (RQD) cannot fully reflect the anisotropy of the rock mass and thus cannot accurately reflect its quality. In the method of calculating RQD based on three-dimensional network simulation of discontinuities, due to the limited number of samples and low accuracy of discontinuity data obtained by manual contact measurement, a certain deviation in the network is generated based on the data, which has an impact on the calculation result. Taking a typical slope in Dongsheng quarry in Changchun City as an example, in this study, we obtained the discontinuity data of the slope based on digital close-range photogrammetry, which greatly enlarged the sample size of discontinuity data and improved the data quality. Based on the heterogeneity of the rock mass, the optimum threshold of discontinuity spacing was determined when surveying lines were laid parallel to different coordinate axes to calculate the generalized RQD, and the influence of measuring blank areas on the slope caused by vegetation coverage or gravel accumulation was eliminated. The real generalized RQD of the rock mass after eliminating the influence of blank areas was obtained. Experiments showed that, after eliminating the influence of blank areas, the generalized RQD of the slope rock mass more truly represented the complete quality of rock mass and offers a new idea for the quality evaluation of engineering rock mass. Full article

(This article belongs to the Special Issue Comprehensive Risk Assessment Methods and Hazards Early Warning System of Geological Hazards in the Mountain Area)

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26 pages, 20405 KiB

Open AccessArticle

Landslide Susceptibility Mapping along a Rapidly Uplifting River Valley of the Upper Jinsha River, Southeastern Tibetan Plateau, China

by Xiaohui Sun, Jianping Chen, Yanrong Li and Ngambua N. Rene

Remote Sens. 2022, 14(7), 1730; https://doi.org/10.3390/rs14071730 - 03 Apr 2022

Cited by 13 | Viewed by 2630

Abstract

As a result of the influence of plate movement, the upper reaches of Jinsha River have strong geological tectonic activities, large topographic fluctuations, and complex climate characteristics, which result in the frequent occurrence of landslide disasters. Hence, there is the need to carry [...] Read more.

As a result of the influence of plate movement, the upper reaches of Jinsha River have strong geological tectonic activities, large topographic fluctuations, and complex climate characteristics, which result in the frequent occurrence of landslide disasters. Hence, there is the need to carry out landslide susceptibility mapping in the upper reaches of Jinsha River to ensure the safety of local people’s property and the safe exploitation of hydraulic resources. In this study, InSAR technology and a field geological survey were used to map the landslides. Then, the curvature watershed method was used to divide the slope units. A conditioning factor system was established, which can reflect the characteristics of the rapid uplift and vertical distribution of rainfall in the special geological environment of the study area. Finally, logistic regression, random forest, and artificial neural network models were used to establish the landslide susceptibility model. The results show that the random forest model is optimal for the landslide susceptibility mapping in this area. Additionally, the area percentages of the very low, low, moderate, high, and very high susceptibility classes were 40.13%, 20.06%, 13.39%, 12.55%, and 13.87%, respectively. Based on the analysis of the landslide susceptibility map, we suggest that the landslide geological hazards resulting from the rapid uplift of the Tibetan Plateau and the significant decrease in sea level during a glacial period in the upper reaches of Jinsha River are controlled by the double disaster effect of the geodynamic system. Consequently, this study can guide local prevention and mitigation. Full article

(This article belongs to the Special Issue Comprehensive Risk Assessment Methods and Hazards Early Warning System of Geological Hazards in the Mountain Area)

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27 pages, 9487 KiB

Open AccessArticle

Failure Mechanism and Long Short-Term Memory Neural Network Model for Landslide Risk Prediction

by Xuan Zhang, Chun Zhu, Manchao He, Menglong Dong, Guangcheng Zhang and Faming Zhang

Remote Sens. 2022, 14(1), 166; https://doi.org/10.3390/rs14010166 - 31 Dec 2021

Cited by 35 | Viewed by 3111

Abstract

Rockslides along a stepped failure surface have characteristics of stepped deformation characteristic and it is difficult to predict the failure time. In this study, the deformation characteristics and disaster prediction model of the Fengning granite rockslide were analyzed based on field surveys and [...] Read more.

Rockslides along a stepped failure surface have characteristics of stepped deformation characteristic and it is difficult to predict the failure time. In this study, the deformation characteristics and disaster prediction model of the Fengning granite rockslide were analyzed based on field surveys and monitoring data. To evaluate the stability, the shear strength parameters of the sliding surface were determined based on the back-propagation neural network and three-dimensional discrete element numerical method. Through the correlation analysis of deformation monitoring results with rainfall and blasting, it is shown that the landslide was triggered by excavation, rainfall, and blasting vibrations. The landslide displacement prediction model was established by using long short-term memory neural network (LSTM) based on the monitoring data, and the prediction results are compared with those using the BP model, SVM model and ARMA model. Results show that the LSTM model has strong advantages and good reliability for the stepped landslide deformation with short-term influence, and the predicted LSTM values were very consistent with the measured values, with a correlation coefficient of 0.977. Combined with the distribution characteristics of joints, the damage influence scope of the landslide was simulated by three-dimensional discrete element, which provides decision-making basis for disaster warning after slope instability. The method proposed in this paper can provide references for early warning and treatment of geological disasters. Full article

(This article belongs to the Special Issue Comprehensive Risk Assessment Methods and Hazards Early Warning System of Geological Hazards in the Mountain Area)

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Other

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15 pages, 12538 KiB

Open AccessTechnical Note

Identification of the Potential Critical Slip Surface for Fractured Rock Slope Using the Floyd Algorithm

by Shengyuan Song, Mingyu Zhao, Chun Zhu, Fengyan Wang, Chen Cao, Haojie Li and Muye Ma

Remote Sens. 2022, 14(5), 1284; https://doi.org/10.3390/rs14051284 - 05 Mar 2022

Cited by 13 | Viewed by 1897

Abstract

A rock slope can be characterized by tens of persistent discontinuities. A slope can be massive. The slip surface of the slope is usually easier to expand along with the discontinuities because the shear strength of the discontinuities is substantially lower than that [...] Read more.

A rock slope can be characterized by tens of persistent discontinuities. A slope can be massive. The slip surface of the slope is usually easier to expand along with the discontinuities because the shear strength of the discontinuities is substantially lower than that of the rock blocks. Based on this idea, this paper takes a jointed rock slope in Hengqin Island, Zhuhai as an example, and establishes a three-dimensional (3D) model of the studied slope by digital close-range photogrammetry to rapidly interpret 222 fracture parameters. Meanwhile, a new Floyd algorithm for finding the shortest path is developed to realize the critical slip surface identification of the studied slope. Within the 3D fracture network model created using the Monte Carlo method, a sequence of cross-sections is placed. These cross-sections containing fractures are used to search for the shortest paths between the designated shear entrances and exits. For anyone combination of entry point and exit point, the shortest paths corresponding to different cross-sections are different and cluttered. For the sake of safety and convenience, these shortest paths are simplified as a circular arc that is regarded as a potential slip surface. The fracture frequency is used to determine the probability of sliding along a prospective critical slip surface. The potential slip surface through the entrance point (0, 80) and exit point (120, 0) is identified as the final critical slip surface of the slope due to the maximum fracture frequency. Full article

(This article belongs to the Special Issue Comprehensive Risk Assessment Methods and Hazards Early Warning System of Geological Hazards in the Mountain Area)

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