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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 15723

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Special Issue Editors

Prof. Dr. Zheng Han

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Guest Editor

Department of Road Engineering, Central South University, Changsha, China
Interests: numerical simulation of geological hazards; hazard prevention and mitigation; hazard detection using RS; hazard assessment

Prof. Dr. Baofeng Di

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Guest Editor

Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu, China
Interests: disaster data science and risk assessment; disaster information system and emergency management
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Dear Colleagues,

Mitigation and prevention against geological hazards, e.g., landslides, debris flows, and collapse, are strongly dependent on hazard assessment and management. The use of remote sensing (RS) and geographic information systems (GISs), providing abilities for spatial and temporal data and relevant analysis tools, has become an integrated and beneficial solution for this purpose. They all contribute in a complementary way to traditional geological hazard assessment methods.

This Special Issue aims to provide an overview and share state-of-the-art and scientific knowledge in recent research and applications in RS and GIS for geological hazard assessment. The topics include new concepts, models, technologies, and recent case studies using GIS and RS techniques to study monitoring, mapping, risk evaluation, and assessment of geological hazards, as well as their disaster chains. These topics can be studied at different scales, from regional to the individual hazard, always looking for a novel vision oriented toward a better understanding, recognition, and risk assessment of geological hazards.

Themes of interest comprise (but are not limited to) the following potential topics:

Applications of new Earth observation products, non-contacting technologies for identifying and detecting geological hazards;
Advances in geological hazard risk assessment models boosted by machine learning;
Geological hazard assessment methods coupling RS and GIS;
GIS-based numerical simulation for reproducing geological processes and delineation of hazard extent;
Latest practical uses for remote sensing and geographic information systems (GIS) for geological hazard assessment.

Prof. Dr. Zheng Han
Prof. Dr. Baofeng Di
Guest Editors

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Keywords

geological hazard
remote sensing
geographic information system
risk assessment
statistical and machine learning methods
GIS-based hazard modeling
risk assessment model

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Published Papers (6 papers)

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Research

16 pages, 5108 KiB

Open AccessFeature PaperArticle

Hierarchical Statistics-Based Nonlinear Vertical Velocity Distribution of Debris Flow and Its Application in Entrainment Estimation

by Zheng Han, Chuicheng Zeng and Yange Li

Water 2022, 14(9), 1352; https://doi.org/10.3390/w14091352 - 21 Apr 2022

Cited by 2 | Viewed by 1919

Abstract

The vertical distribution of debris flow profile velocity is the key to studying debris flow, impulse and the sediment carrying process. At present, the linear distribution model based on flume test results cannot describe the vertical distribution of debris flow velocity effectively due to the limitation of measurement methods. In this paper, the smooth particle hydrodynamics (SPH) numerical model based on the Herschel–Bulkley–Papanastasiou (HBP) constitutive model is utilized to invert the three-dimensional dynamic process of debris flow based on a large-scale debris flow flume experiment. With a hierarchical statistical approach, a huge number of particle velocity data were analyzed and processed to obtain the vertical distribution law of velocity. We proposed a nonlinear vertical distribution model of debris flow velocity based on logarithm function accordingly. We also applied the proposed model to the existing debris flow entrainment estimation framework. A flume dam break test case was inverted to verify the performance of erosion calculations. The results show that the numerical simulation results of erosion depth are close to the experimental values. The error percentage of maximum erosion depth is 4.1%. The average error percentage of erosion depth simulation results is 15.5%. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geological Hazards Assessment)

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16 pages, 2354 KiB

Open AccessArticle

Predicting Change in Adaptation Strategies of Households to Geological Hazards in the Longmenshan Area, China Using Machine Learning and GIS

by Haichuan Su, Glenn Fernandez, Xiaoxi Hu, Shaolin Wu, Baofeng Di and Chunping Tan

Water 2022, 14(7), 1023; https://doi.org/10.3390/w14071023 - 24 Mar 2022

Cited by 1 | Viewed by 2113

Abstract

Hydrological changes combined with earthquakes easily trigger secondary disasters, including geological hazards. The secondary hazard of precipitation is the main disaster type in the Longmenshan Area (China). The 2008 Wenchuan earthquake caused more than 60,000 landslides, severely affecting rural households. This study aimed to answer two questions: (1) How did households adapt to the landslide-prone post-earthquake environment? (2) How will the households’ adaptation strategies change if landslide frequency changes? Different post-disaster adaptation strategies of households in Longmenshan Town, Sichuan, China were identified through a questionnaire survey and then clustered into groups based on similarity using a K-means algorithm. Afterward, a gradient boosting decision tree (GBDT) was used to predict change in adaptation strategies if there was a change in the frequency of landslides. The results show that there are three types of landslide adaptation strategies in the study area: (1) autonomous adaptation; (2) policy-dependent adaptation; and (3) hybrid adaptation, which is a mixture of the first two types. If the frequency of landslides is increased, then around 5% of households previously under the autonomous adaptation type would be converted to policy-dependent and hybrid adaptation types. If the frequency of landslides is reduced, then around 5% of households with policy-dependent adaptation strategies would be converted to the autonomous adaptation type. This exploratory study provides a glimpse of how machine learning can be utilized to predict how adaptation strategies would be modified if hazard frequency changed. A follow-up long-term study in Longmenshan Town is needed to confirm whether the predictions are indeed correct. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geological Hazards Assessment)

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22 pages, 6306 KiB

Open AccessFeature PaperArticle

Spatial Non-Stationarity-Based Landslide Susceptibility Assessment Using PCAMGWR Model

by Yange Li, Shuangfei Huang, Jiaying Li, Jianling Huang and Weidong Wang

Water 2022, 14(6), 881; https://doi.org/10.3390/w14060881 - 11 Mar 2022

Cited by 3 | Viewed by 2471

Abstract

Landslide Susceptibility Assessment (LSA) is a fundamental component of landslide risk management and a substantial area of geospatial research. Previous researchers have considered the spatial non-stationarity relationship between landslide occurrences and Landslide Conditioning Factors (LCFs) as fixed effects. The fixed effects consider the spatial non-stationarity scale between different LCFs as an average value, which is represented by a single bandwidth in the Geographically Weighted Regression (GWR) model. The present study analyzes the non-stationarity scale effect of the spatial relationship between LCFs and landslides and explains the influence of factor correlation on the LSA. A Principal-Component-Analysis-based Multiscale GWR (PCAMGWR) model is proposed for landslide susceptibility mapping, in which hexagonal neighborhoods express spatial proximity and extract LCFs as the model input. The area under the receiver operating characteristic curve and other statistical indicators are used to compare the PCAMGWR model with other GWR-based models and global regression models, and the PCAMGWR model has the best prediction effect. Different spatial non-stationarity scales are obtained and improve the prediction accuracy of landslide susceptibility compared to a single spatial non-stationarity scale. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geological Hazards Assessment)

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17 pages, 9782 KiB

Open AccessFeature PaperArticle

Generation of Homogeneous Slope Units Using a Novel Object-Oriented Multi-Resolution Segmentation Method

by Yange Li, Jianhua He, Fang Chen, Zheng Han, Weidong Wang, Guangqi Chen and Jianling Huang

Water 2021, 13(23), 3422; https://doi.org/10.3390/w13233422 - 3 Dec 2021

Cited by 6 | Viewed by 2516

Abstract

The generation of map units is a fundamental step for an appropriate assessment of landslide susceptibility. Recent studies have indicated that the terrain relief-based slope units perform better in homogeneity compared with the grid units. However, it is difficult at present to generate high-precision and high-matching slope units by traditional methods. The problem commonly concentrates in the plain areas without obvious terrain reliefs and the junction of sudden changes in terrain. In this paper, we propose a novel object-oriented segmentation method for generating homogeneous slope units. Herein, the multi-resolution segmentation algorithm in the image processing field is introduced, enabling the integration of terrain boundary conditions and image segmentation conditions in slope units. In order to illustrate the performances of the proposed method, Kitakyushu region in Japan is selected as a case study. The results show that the proposed method generates satisfactory slope units that satisfactorily reproduce the actual terrain relief, with the best within-unit and between-unit homogeneities compared with the previous methods, in particular at the plain areas. We also verify the effectiveness of the presented method through the sensitivity analysis using different resolutions of digital elevation models (DEMs) data of the region. It is reported that the presented approach is notably advanced in the requirements of the quality of DEM data, as the presented approach is less sensitive to DEM spatial resolution compared with other available methods. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geological Hazards Assessment)

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16 pages, 4655 KiB

Open AccessArticle

Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan

by Jiaying Li, Weidong Wang, Yange Li, Zheng Han and Guangqi Chen

Water 2021, 13(22), 3312; https://doi.org/10.3390/w13223312 - 22 Nov 2021

Cited by 8 | Viewed by 2816

Abstract

Landslide represents an increasing menace causing huge casualties and economic losses, and rainfall is a predominant factor inducing landslides. Landslide susceptibility assessment (LSA) is a commonly used and effective method to prevent landslide risk, however, the LSA does not analyze the impact of the rainfall on landslides which is significant and non-negligible. Therefore, the spatiotemporal LSA considering the inducing effect of rainfall is proposed to improve accuracy and applicability. In this study, the influencing factors are selected using the chi-square test, out-of-bag error and multicollinearity test. The spatial LSA are thus obtained using the random forest (RF) model, deep belief networks model and support vector machine, and compared using receiver operating characteristic curve and seed cell area index to determine the optimal assessment result. According to the heavy rainfall characteristics in the study area, the rainfall period is divided into four stages, and the effective rainfall model is employed to generate the rainfall impact (RI) maps of the four stages. The spatiotemporal LSAs are obtained by coupling the optimal spatial LSA and various RI maps and verified using the landslide warning map. The results demonstrate that the optimal spatiotemporal LSA is obtained using the spatial LSA of the RF model and temporal LSA of the rainfall data in the peak stage. It can predict the area where rainfall-induced landslides are likely to occur and prevent landslide risk. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geological Hazards Assessment)

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18 pages, 7793 KiB

Open AccessFeature PaperArticle

GIS-Based Three-Dimensional SPH Simulation for the 11 April 2018 Yabakei Landslide at Oita Nakatsu, Japan

by Zheng Han, Fan Yang, Yange Li, Jie Dou, Ningsheng Chen, Guisheng Hu, Guangqi Chen and Linrong Xu

Water 2021, 13(21), 3012; https://doi.org/10.3390/w13213012 - 27 Oct 2021

Cited by 9 | Viewed by 2705

Abstract

Landslides are usually triggered by strong earthquakes, heavy rainfalls, or intensive human activities in common wisdom. However, an unexpected landslide occurred in the Yabakei area, Nakatsu, Oita, Japan, at the pre-dawn hour 3:50 a.m. on 11 April 2018, without any accompanying rainfall and earthquake records during the event. This catastrophic landslide was 200 m in width, 110 m in height, and 60,000 m³ in mass volume, damaging four residential buildings with fatalities of six residents at the landslide toe. Field investigation was conducted immediately to identify geological setting, hydrological condition, and landslide geomorphological characteristics. Key findings speculate that infiltration of groundwater stored in the internal fractures led to the swelling and breaking of illite and askanite in the weathered sediment rocks, resulting in the failure of the Yabakei landslide. To reproduce and explore the dynamic process of this landslide event, based on spatial GIS data, we applied the proposed three-dimensional, Herschel-Bulkley-Papanastasiou rheology model-based smooth particle hydrodynamics (HBP-SPH) method to simulate the landslide dynamic process. Buildings in the landslide area are covered by a set of surfaced cells (SC) to analyze the mass impact on the residential buildings. Results showed good accordance between observation and simulation by the proposed SC-HBP-SPH method. The landslide impact force to the residential buildings could be up to 4224.89 kN, as indicated by the simulation. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Geological Hazards Assessment)

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