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6 pages, 1122 KiB

Open AccessEditorial

“Novel Approaches in Landslide Monitoring and Data Analysis” Special Issue: Trends and Challenges

by Jan Blahůt, Michel Jaboyedoff and Benni Thiebes

Appl. Sci. 2021, 11(21), 10453; https://doi.org/10.3390/app112110453 - 7 Nov 2021

Cited by 5 | Viewed by 1998

Abstract

The purpose of this Special Issue is to bring together recent studies related in particular to landslide monitoring and data analysis [...] Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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Research

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29 pages, 13190 KiB

Open AccessArticle

Airborne Electromagnetics to Improve Landslide Knowledge in Tropical Volcanic Environments

by Yannick Thiery, Pierre-Alexandre Reninger and Aude Nachbaur

Appl. Sci. 2021, 11(8), 3390; https://doi.org/10.3390/app11083390 - 9 Apr 2021

Cited by 8 | Viewed by 2125

Abstract

Caribbean areas are particular volcanic territories in tropical environments. These territories juxtapose several landslide-prone areas with different predisposing factors (poorly consolidated volcanic materials, superimposition of healthy materials on highly weathered materials, high heterogeneity of thicknesses, etc.). In these environments, where rapid development of [...] Read more.

Caribbean areas are particular volcanic territories in tropical environments. These territories juxtapose several landslide-prone areas with different predisposing factors (poorly consolidated volcanic materials, superimposition of healthy materials on highly weathered materials, high heterogeneity of thicknesses, etc.). In these environments, where rapid development of slopes and land use changes are noticeable, it is necessary to better characterize these unstable phenomena that cause damage to infrastructure and people. This characterization has to be carried out on the materials as well as on the initiation conditions of the phenomena and requires complementary investigations. This study, focusing on La Martinique, proposes a landslide analysis methodology that combines new information about landslide-prone materials acquired by an airborne electromagnetics survey with a physical-based model. Once the data are interpreted and compared with field observations and previous data, a geological model is produced and introduced into the physical model to test different instability scenarios. The results show that geophysical investigations (i) improve the knowledge of the internal structure of landslides and surficial formations, (ii) specify the spatial limits of the materials that are sensitive to landslides, and (iii) give a better understanding of landslide initiation conditions, particularly hydrogeological triggering conditions. Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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20 pages, 10762 KiB

Open AccessArticle

Spatial Uncertainty of Target Patterns Generated by Different Prediction Models of Landslide Susceptibility

by Andrea G. Fabbri and Antonio Patera

Appl. Sci. 2021, 11(8), 3341; https://doi.org/10.3390/app11083341 - 8 Apr 2021

Cited by 4 | Viewed by 1634

Abstract

This contribution exposes the relative uncertainties associated with prediction patterns of landslide susceptibility. The patterns are based on relationships between direct and indirect spatial evidence of landslide occurrences. In a spatial database constructed for the modeling, direct evidence is the presence of landslide [...] Read more.

This contribution exposes the relative uncertainties associated with prediction patterns of landslide susceptibility. The patterns are based on relationships between direct and indirect spatial evidence of landslide occurrences. In a spatial database constructed for the modeling, direct evidence is the presence of landslide trigger areas, while indirect evidence is the presence of corresponding multivariate context in the form of digital maps. Five mathematical modeling functions are applied to capture and integrate evidence, indirect and direct, for separating landslide-presence areas from the areas of landslide assumed absence. Empirical likelihood ratios are used first to represent the spatial relationships. These are then combined by the models into prediction scores, ordered, equal-area ranked, displayed, and synthesized as prediction-rate curves. A critical task is assessing how uncertainty levels vary across the different prediction patterns, i.e., the modeling results visualized as fixed, colored groups of ranks. This is obtained by a strategy of iterative cross validation that uses only part of the direct evidence to model the pattern and the rest to validate it as a predictor. The conducted experiments in a mountainous area in northern Italy point at a research challenge that can now be confronted with relative rank-based statistics and iterative cross-validation processes. The uncertainty properties of prediction patterns are mostly unknown nevertheless they are critical for interpreting and justifying prediction results. Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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22 pages, 14792 KiB

Open AccessFeature PaperArticle

Past, Present and Future Monitoring at the Vallcebre Landslide (Eastern Pyrenees, Spain)

by Josep A. Gili, Jose Moya, Jordi Corominas, Michele Crosetto and Oriol Monserrat

Appl. Sci. 2021, 11(2), 571; https://doi.org/10.3390/app11020571 - 8 Jan 2021

Cited by 9 | Viewed by 2730

Abstract

Works carried out to monitor the displacements of the Vallcebre landslide (Pyrenees range, NE of Spain) since 1987 are presented. The landslide, which extends over an area of about 0.8 km² and affects more than 20 × 10⁶ m³, [...] Read more.

Works carried out to monitor the displacements of the Vallcebre landslide (Pyrenees range, NE of Spain) since 1987 are presented. The landslide, which extends over an area of about 0.8 km² and affects more than 20 × 10⁶ m³, has experienced displacements of up to one meter per year in some points and periods. It has been periodically monitored since 1987, using a wide range of surface and in-hole techniques: triangulation with theodolite, Terrestrial Photogrammetry, Electronic Distance Measurement, GNSS-GPS, inclinometers, wire extensometers, piezometers, DInSAR (satellite) and GBSAR (terrestrial). The results obtained using new techniques are compared with those obtained with GNSS-GPS and a wire extensometer, and checked against fixed stable points. From this comparison, we conclude that even though wire extensometers and inclinometers may have the highest precision, in practice, all systems play potentially valuable roles in providing meaningful data for monitoring at different study stages. In the near future, we envisage the installation of a Distributed Fiber Optic array to monitor the risk with a certain space and time continuity. After the evaluation of the precision and advantages of the different methods, the complementary use of some of them is strongly recommended. Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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24 pages, 12583 KiB

Open AccessArticle

New Landslide Disaster Monitoring System: Case Study of Pingding Village

by Yao Min Fang, Tien Yin Chou, Thanh Van Hoang, Quang Thanh Bui, Duy Ba Nguyen and Quoc Huy Nguyen

Appl. Sci. 2020, 10(19), 6718; https://doi.org/10.3390/app10196718 - 25 Sep 2020

Cited by 2 | Viewed by 3565

Abstract

The Linbeiken area is located in the village of Pingding, Taiwan. Since the Mindulle and Aere Typhoons in 2004, and as a result of the landslide triggered by the continuous heavy rainfall on 9 June 2006, there has been a persistent collapse of [...] Read more.

The Linbeiken area is located in the village of Pingding, Taiwan. Since the Mindulle and Aere Typhoons in 2004, and as a result of the landslide triggered by the continuous heavy rainfall on 9 June 2006, there has been a persistent collapse of side slopes in the area. This paper describes the equipment that was installed to collect on-site topographic and hydrological information in the Linbeiken area upstream of the Pingding River and to monitor changes in the landslide area, as well as the measurements that were collected during the 2008 Typhoon Sinlaku. A case study of a landslide in Pingding, Taiwan was used to monitor the accurate coordinate changes in the potential landslide areas during typhoons. The goal of this study was to establish warning indexes, and to strengthen the software and hardware at the local disaster response center in the hope of gaining a full idea of the surface movement in landslide areas in future flood seasons. This is important for boosting the preparedness to adapt to landslide hazards, for improving disaster warnings, and for reporting efficiently to better protect the lives and property of local residents. The results show that the landslide disaster monitoring and warning system in Taiwan, as applied during Typhoon Sinlaku in 2008, is both effective and comprehensive. Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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23 pages, 14599 KiB

Open AccessArticle

Separating Landslide Source and Runout Signatures with Topographic Attributes and Data Mining to Increase the Quality of Landslide Inventory

by Jhe-Syuan Lai

Appl. Sci. 2020, 10(19), 6652; https://doi.org/10.3390/app10196652 - 23 Sep 2020

Cited by 6 | Viewed by 2539

Abstract

Landslide sources and runout features of typical natural terrain landslides can be observed from a geotechnical perspective. Landslide sources are the major area of occurrences, whereas runout signatures reveal the subsequent phenomena caused by unstable gravity. Remotely sensed landslide detection generally includes runout [...] Read more.

Landslide sources and runout features of typical natural terrain landslides can be observed from a geotechnical perspective. Landslide sources are the major area of occurrences, whereas runout signatures reveal the subsequent phenomena caused by unstable gravity. Remotely sensed landslide detection generally includes runout areas, unless these results have been excluded manually through detailed comparison with stereo aerial photos and other auxiliary data. Areas detected using remotely sensed landslide detection can be referred to as “landslide-affected” areas. The runout areas should be separated from landslide-affected areas when upgrading landslide detections into a landslide inventory to avoid unreliable results caused by impure samples. A supervised data mining procedure was developed to separate landslide sources and runout areas based on four topographic attributes derived from a 10–m digital elevation model with a random forest algorithm and cost-sensitive analysis. This approach was compared with commonly used methods, namely support vector machine (SVM) and logistic regression (LR). The Typhoon Morakot event in the Laonong River watershed, southern Taiwan, was modeled. The developed models constructed using the limited training data sets could separate landslide source and runout signatures verified using the polygon and area constraint-based datasets. Furthermore, the performance of developed models outperformed SVM and LR algorithms, achieving over 80% overall accuracy, area under the curve of the receiver operating characteristic, user’s accuracy, and producer’s accuracy in most cases. The agreement of quantitative evaluations between the area sizes of inventory polygons for training and the predicted targets was also observed when applying the supervised modeling strategy. Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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15 pages, 13593 KiB

Open AccessArticle

Study on the Early Warning Methods of Dynamic Landslides of Large Abandoned Rockfill Slopes

by Nan Qiao, Yun-Ling Duan, Xiao-Meng Shi, Xue-Fei Wei and Jin-Ming Feng

Appl. Sci. 2020, 10(17), 6097; https://doi.org/10.3390/app10176097 - 2 Sep 2020

Cited by 4 | Viewed by 2305

Abstract

The excavation of large-scale underground projects produces a large amount of rubble waste material that is temporarily deposited near the project site, which forms a large-scale waste rockfill artificial slope. The slope has a granular structure, thus, during excavation and trans-shipment, surface shallow [...] Read more.

The excavation of large-scale underground projects produces a large amount of rubble waste material that is temporarily deposited near the project site, which forms a large-scale waste rockfill artificial slope. The slope has a granular structure, thus, during excavation and trans-shipment, surface shallow landslides may frequently occur. Existing contact monitoring methods such as buried sensors and GPS (Global Position System) are difficult to apply to the monitoring of rockfill landslides. Therefore, there are no appropriate early warning methods for waste rockfill slope landslides during dynamic transfer. Here, we used ground-based interferometric synthetic aperture radar to monitor the deformation of a rockfill slope during the excavation and transfer processes as a proposed method for the early warning against landslides on rockfill slopes during dynamic construction based on the radar interference measurement results. Through data cleaning and data interpolation, the line of equal displacement was generated, and the cross-sectional area of the equal displacement bodies of landslides was calculated. In addition, we established a four-level early warning grading standard, with the rate of change of the cross-sectional area of the equal displacement body as the early warning index, and realized real-time dynamic early warning of waste rockfill landslides during excavation and transportation. Finally, five landslide examples were used to verify the proposed warning method. The results show that the warning method can make an early warning 8–14 min before the occurrence of landslide, which can effectively avoid the appearance of catastrophic events. Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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20 pages, 6208 KiB

Open AccessArticle

Assessment of Landslide-Induced Geomorphological Changes in Hítardalur Valley, Iceland, Using Sentinel-1 and Sentinel-2 Data

by Zahra Dabiri, Daniel Hölbling, Lorena Abad, Jón Kristinn Helgason, Þorsteinn Sæmundsson and Dirk Tiede

Appl. Sci. 2020, 10(17), 5848; https://doi.org/10.3390/app10175848 - 24 Aug 2020

Cited by 22 | Viewed by 5370

Abstract

Landslide mapping and analysis are essential aspects of hazard and risk analysis. Landslides can block rivers and create landslide-dammed lakes, which pose a significant risk for downstream areas. In this research, we used an object-based image analysis approach to map geomorphological features and [...] Read more.

Landslide mapping and analysis are essential aspects of hazard and risk analysis. Landslides can block rivers and create landslide-dammed lakes, which pose a significant risk for downstream areas. In this research, we used an object-based image analysis approach to map geomorphological features and related changes and assess the applicability of Sentinel-1 data for the fast creation of post-event digital elevation models (DEMs) for landslide volume estimation. We investigated the Hítardalur landslide, which occurred on the 7 July 2018 in western Iceland, along with the geomorphological changes induced by this landslide, using optical and synthetic aperture radar data from Sentinel-2 and Sentinel-1. The results show that there were no considerable changes in the landslide area between 2018 and 2019. However, the landslide-dammed lake area shrunk between 2018 and 2019. Moreover, the Hítará river diverted its course as a result of the landslide. The DEMs, generated by ascending and descending flight directions and three orbits, and the subsequent volume estimation revealed that—without further post-processing—the results need to be interpreted with care since several factors influence the DEM generation from Sentinel-1 imagery. Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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18 pages, 4138 KiB

Open AccessArticle

Bayesian Updating of Soil–Water Character Curve Parameters Based on the Monitor Data of a Large-Scale Landslide Model Experiment

by Chengxin Feng, Bin Tian, Xiaochun Lu, Michael Beer, Matteo Broggi, Sifeng Bi, Bobo Xiong and Teng He

Appl. Sci. 2020, 10(16), 5526; https://doi.org/10.3390/app10165526 - 10 Aug 2020

Cited by 9 | Viewed by 2765

Abstract

It is important to determine the soil–water characteristic curve (SWCC) for analyzing landslide seepage under varying hydrodynamic conditions. However, the SWCC exhibits high uncertainty due to the variability inherent in soil. To this end, a Bayesian updating framework based on the experimental data [...] Read more.

It is important to determine the soil–water characteristic curve (SWCC) for analyzing landslide seepage under varying hydrodynamic conditions. However, the SWCC exhibits high uncertainty due to the variability inherent in soil. To this end, a Bayesian updating framework based on the experimental data was developed to investigate the uncertainty of the SWCC parameters in this study. The objectives of this research were to quantify the uncertainty embedded within the SWCC and determine the critical factors affecting an unsaturated soil landslide under hydrodynamic conditions. For this purpose, a large-scale landslide experiment was conducted, and the monitored water content data were collected. Steady-state seepage analysis was carried out using the finite element method (FEM) to simulate the slope behavior during water level change. In the proposed framework, the parameters of the SWCC model were treated as random variables and parameter uncertainties were evaluated using the Bayesian approach based on the Markov chain Monte Carlo (MCMC) method. Observed data from large-scale landslide experiments were used to calculate the posterior information of SWCC parameters. Then, 95% confidence intervals for the model parameters of the SWCC were derived. The results show that the Bayesian updating method is feasible for the monitoring of data of large-scale landslide model experiments. The establishment of an artificial neural network (ANN) surrogate model in the Bayesian updating process can greatly improve the efficiency of Bayesian model updating. Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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16 pages, 3798 KiB

Open AccessArticle

3D Dilatometer Time-Series Analysis for a Better Understanding of the Dynamics of a Giant Slow-Moving Landslide

by Jan Blahůt, Jan Balek, Michal Eliaš and Stavros Meletlidis

Appl. Sci. 2020, 10(16), 5469; https://doi.org/10.3390/app10165469 - 7 Aug 2020

Cited by 6 | Viewed by 3305

Abstract

This paper presents a methodological approach to the time-series analysis of movement monitoring data of a large slow-moving landslide. It combines different methods of data manipulation to decrease the subjectivity of a researcher and provides a fully quantitative approach for analyzing large amounts [...] Read more.

This paper presents a methodological approach to the time-series analysis of movement monitoring data of a large slow-moving landslide. It combines different methods of data manipulation to decrease the subjectivity of a researcher and provides a fully quantitative approach for analyzing large amounts of data. The methodology was applied to 3D dilatometric data acquired from the giant San Andrés Landslide on El Hierro in the Canary Islands in the period from October 2013 to April 2019. The landslide is a creeping volcanic flank collapse showing a decrease of speed of movement during the monitoring period. Despite the fact that clear and unambiguous geological interpretations cannot be made, the analysis is capable of showing correlations of the changes of the movement with increased seismicity and, to some point, with precipitation. We consider this methodology being the first step in automatizing and increasing the objectivity of analysis of slow-moving landslide monitoring data. Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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18 pages, 7578 KiB

Open AccessArticle

Integrated Field Surveying and Land Surface Quantitative Analysis to Assess Landslide Proneness in the Conero Promontory Rocky Coast (Italy)

by Francesco Troiani, Salvatore Martino, Gian Marco Marmoni, Marco Menichetti, Davide Torre, Giulia Iacobucci and Daniela Piacentini

Appl. Sci. 2020, 10(14), 4793; https://doi.org/10.3390/app10144793 - 13 Jul 2020

Cited by 11 | Viewed by 3063

Abstract

Rock slopes involved in extensive landslide processes are often characterized by complex morphodynamics acting at different scales of space and time, responsible for different evolutionary scenarios. Mass Rock Creep (MRC) is a critical process for long-term geomorphological evolution of slopes and can likewise [...] Read more.

Rock slopes involved in extensive landslide processes are often characterized by complex morphodynamics acting at different scales of space and time, responsible for different evolutionary scenarios. Mass Rock Creep (MRC) is a critical process for long-term geomorphological evolution of slopes and can likewise characterize actively retreating coastal cliffs where, in addition, landslides of different typologies and size superimpose in space and time to marine processes. The rocky coast at the Conero promontory (central Adriatic Sea, Italy) offers a rare opportunity for better understanding the predisposing role of the morphostructural setting on coastal slope instability on a long-time scale. In fact, the area presents several landslides of different typologies and size and state of activity, together with a wide set of landforms and structural features effective for better comprehending the evolution mechanisms of slope instability processes. Different investigation methods were implemented; in particular, traditional geomorphological and structural field surveys were combined with land surface quantitative analysis based on a Digital Elevation Model (DEM) with ground-resolution of 2 m. The results obtained demonstrate that MRC involves the entire coastal slope, which can be zoned in two distinct sectors as a function of a different morphostructural setting responsible for highly differentiated landslide processes. Therefore, at the long-time scale, two different morphodynamic styles can be depicted along the coastal slopes that correspond to specific evolutionary scenarios. The first scenario is characterized by MRC-driven, time-dependent slope processes involving the entire slope, whereas the second one includes force-driven slope processes acting at smaller space–time scales. The Conero promontory case study highlights that the relationships between slope shape and structural setting of the deforming areas are crucial for reaching critical volumes to induce generalized slope collapse as the final stage of the MRC process. The results from this study stress the importance of understanding the role of morphostructures as predisposing conditions for generalized slope failures along rocky coasts involved in MRC. The findings discussed here suggest the importance of the assessment of the slope instability at the long time scale for a better comprehension of the present-day slope dynamics and its major implications for landslide monitoring strategies and the hazard mitigation strategies. Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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21 pages, 6998 KiB

Open AccessArticle

Physical Model Experiments on Water Infiltration and Failure Modes in Multi-Layered Slopes under Heavy Rainfall

by Junfeng Tang, Uchimura Taro, Dong Huang, Jiren Xie and Shangning Tao

Appl. Sci. 2020, 10(10), 3458; https://doi.org/10.3390/app10103458 - 17 May 2020

Cited by 16 | Viewed by 3481

Abstract

To assess the influence of an intermediate coarse layer on the slope stability during heavy rainfall, knowledge about water movement and how slope failure occurs is important. To clarify the characteristics of water infiltration in a multi-layered slope and assess its influence on [...] Read more.

To assess the influence of an intermediate coarse layer on the slope stability during heavy rainfall, knowledge about water movement and how slope failure occurs is important. To clarify the characteristics of water infiltration in a multi-layered slope and assess its influence on the slope failure modes, eight groups of physical slope models were investigated. It was found that the unsaturated hydraulic conductivity in the coarse layer (5.54 × 10⁻⁶ cm/s) was much lower than that of the fine layer (1.08 × 10⁻⁴ cm/s), which resulted in the capillary barrier working at a lower water content. Intermediate coarse layers embedded between finer ones may initially confine the infiltration within the overlying finer layers, delaying the infiltration and eventually inducing a lateral flow diversion in the inclined slope. Two different failure modes occurred in the model experiments: surface sliding occurred at the toe in the single-layer slope group and piping occurred at the toe in the multi-layered slope as the rainfall water accumulated, was diverted along the interface, and then broke through in the downslope direction of the intermediate coarse layer. The lateral flow diversion caused by the capillary barrier and the tilt angle may be the major factors influencing the difference of the failure modes. The result also revealed that the coarser layers may have negative effects on the slope stability. Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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17 pages, 7294 KiB

Open AccessArticle

Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides

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

Appl. Sci. 2020, 10(7), 2518; https://doi.org/10.3390/app10072518 - 6 Apr 2020

Cited by 17 | Viewed by 2643

Abstract

Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal [...] Read more.

Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal variation on hazard susceptibility, the southern area of Sichuan province in China is selected as a study area. Multitemporal DEM data spanning over 17 years are collected and the topographic variation of the landscape in this area is investigated. Multitemporal susceptibility maps of landslides are subsequently generated using the widely accepted logistic regression model (LRM). A positive correlation between the topographic variation and landslide susceptibility that was supported by previous studies is quantitatively verified. The ratio of the number of landslides to the susceptibility level areas (RNA) in which the hazards occur is introduced. The RNA demonstrates a general decrease in the susceptibility level from 2000 to 2009, while the ratio of the decreased level is more than fifteen times greater than that of the ratio of the increased level. The impact of the multitemporal DEM on susceptibility mapping is demonstrated to be significant. As such, susceptibility assessments should use DEM data at the time of study. Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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16 pages, 8657 KiB

Open AccessArticle

Spatial Proximity-Based Geographically Weighted Regression Model for Landslide Susceptibility Assessment: A Case Study of Qingchuan Area, China

by Yange Li, Xintong Liu, Zheng Han and Jie Dou

Appl. Sci. 2020, 10(3), 1107; https://doi.org/10.3390/app10031107 - 7 Feb 2020

Cited by 46 | Viewed by 4857

Abstract

Landslides pose a serious threat to the safety of human life and property in mountainous regions. Susceptibility assessment for landslides is critical in landslide management strategy. Recent studies indicate that the traditional assessment models in many previous studies commonly assume a fixed relationship [...] Read more.

Landslides pose a serious threat to the safety of human life and property in mountainous regions. Susceptibility assessment for landslides is critical in landslide management strategy. Recent studies indicate that the traditional assessment models in many previous studies commonly assume a fixed relationship between influencing factors and landslide occurrence within an area, resulting in an inadequate evaluation for the local landslides susceptibility. To address this issue, in this paper we propose a spatial proximity-based geographically weighted regression (S-GWR) model considering spatial non-stationarity of landslide data for assessing the landslide susceptibility. Spatial proximity is the basic input condition for the proposed S-GWR model. The challenge lies in defining the spatial proximity expression that shows the geographical features of landslides and therefore affects the model ability of S-GWR. Our solution chooses the slope unit as spatial adjacency, rather than the grid unit in DTM. The multicollinearity between landslide influencing factors is then eliminated through variance inflation factor (VIF) method and principal component analysis (PCA). The proposed model is subsequently validated by using data in Qingchuan County, southwestern China. Spatial non-stationary is identified for landslide data. A comparison with grid unit and four traditional evaluation models is conducted. Validation results using the area under the ROC (receiver operating characteristic) curve and success rate curve indicate that the spatial proximity-based GWR model with slope unit has the highest predictive accuracy (0.859 and 0.850 respectively). Full article

(This article belongs to the Special Issue Novel Approaches in Landslide Monitoring and Data Analysis)

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