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Assessment of Landslide Susceptibility and Hazard in the Big Data Era

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 23866

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Special Issue Editors

Prof. Hyuck-Jin Park

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Department of Energy Resources and Geosystem Engineering, Sejong University, Seoul 05006, Korea
Interests: landslide hazard; monitoring and modelling of basin scale surface processes; natural hazards; applications of remote sensing to landslide studies; scaling processes in geomorphology

Prof. Dr. Filippo Catani

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Department of Earth Sciences, University of Florence, Engineering Geology and Geomorphology Research Group, Via La Pira 4, 50121 Firenze, Italy
Interests: landslide hazard; monitoring and modelling of basin scale surface processes; natural hazards; applications of remote sensing to landslide studies; oil & gas environmental impact and risk; surface monitoring in open pit mines; scaling processes in geomorphology; machine learning applied to land surface processes
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Prof. Alessandro Simoni

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Department of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, 40126 Bologna, Italy
Interests: landslide and debris flow hazard assessment; field monitoring of landslides; slope hydrology; landslide triggering and propagation; InSAR

Prof. Matteo Berti

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Dipartimento di Scienze Biologiche, Geologiche e Ambientali - Università di Bologna, 40126 Bologna, Italy
Interests: geology; hydrology; landslides

Special Issue Information

Dear Colleagues,

Landslides have been recognized as a major threat to lives and properties in most mountainous regions of the world. Statistics from the Center for Research on Epidemiology of Disasters (CRED) show that landslides are responsible for at least 17% of all fatalities from natural hazards worldwide (Lacasse and Nadim, 2009). Therefore, various studies have been performed to predict landslide occurrences and reduce the damage caused by landslides. Assessments of landslide susceptibility and hazards are generally carried out by scientists from fields such as engineering geology, geomorphology, geophysics, hydrology, soil science, and geography. However, at present, this rapidly growing field is becoming multi-disciplinary as different technologies are integrated in order to achieve a better understanding of landslide initiation mechanisms and processes. Recently, much of the progress has been made by new advancements in technology, such as machine learning, UAVs, satellite images, and simulation models. Most of these new techniques bring a massive volume of both structured and unstructured data, and require specialized algorithms and methods to produce fruitful results. Data science is changing a number of scientific disciplines, offering new opportunities for discovering new knowledge and providing effective tools to simulate complex phenomena. This revolution has just begun, and there is a growing interest in the application of data science methods for landslide studies, both to develop black-box prediction models and to support the classical physics-based methods.

This Special Issue of Applied Sciences aims to encourage researchers to address the recent progress in the field of landslide susceptibility and hazard assessment, taking advantage of the new opportunities in the Big Data Era in topics including, but not limited to, the following:

Analysis of Big Data coming from high-frequency monitoring networks;
Machine learning methods for the assessment of landslide initiation;
Big Data assimilation in landslide numerical models;
Data mining of large data storages from remote sensing;
Model validation through unstructured data mining, and social media data mining.

Prof. Hyuck-Jin Park
Prof. Filippo Catani
Prof. Alessandro Simoni
Prof. Matteo Berti
Guest Editors

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Keywords

Landslide inventory mapping
Landslide hazard recognition and assessment
Data-driven landslide susceptibility assessment
Physically based landslide hazard assessment
Statistical methods for predicting the spatial occurrence of landslides
Temporal prediction of landslide occurrence
Data-driven models to predict temporal occurrence of landslides
Analysis of Big Data from landslide monitoring
Machine learning for landslide prediction
Data mining of Earth Observation for landslide prediction
Big Data assimilation in landslide numerical models
Model validation through unstructured data mining

Published Papers (8 papers)

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Research

18 pages, 7105 KiB

Open AccessArticle

A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey

by Recep Can, Sultan Kocaman and Candan Gokceoglu

Appl. Sci. 2021, 11(11), 4993; https://doi.org/10.3390/app11114993 - 28 May 2021

Cited by 76 | Viewed by 5356

Abstract

The success rate in landslide susceptibility mapping efforts increased with the advancements in machine learning algorithms and the availability of geospatial data with high spatial and temporal resolutions. Existing data-driven susceptibility mapping models are not globally applicable due to the high variability of landslide conditioning parameters and the limitations in the availability of up-to-date and accurate data. Among numerous applications, landslide susceptibility maps are essential for site selection and health monitoring of engineering structures, such as dams, for increasing their lifetime and to prevent from disastrous events caused by the damages. In this study, landslide susceptibility mapping performance of XGBoost algorithm was evaluated in a landslide-prone area in the upper basin of Ataturk Dam, which is a prime investment located in the southeast of Turkey. The study area has a size of 2718.7 km² with an elevation difference of ca. 2000 m and contains 27 lithological units. EU-DEM v1.1 from the Copernicus Programme was used to derive the geomorphological features. High classification accuracy with area under curve value of 0.96 could be obtained from the XGBoost algorithm. According to the results, the main factors controlling the landslides in the study area are the lithology, altitude and topographic wetness index. Full article

(This article belongs to the Special Issue Assessment of Landslide Susceptibility and Hazard in the Big Data Era)

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20 pages, 7903 KiB

Open AccessArticle

Influence of Earthquakes on Landslide Susceptibility in a Seismic Prone Catchment in Central Asia

by Fengqing Li, Isakbek Torgoev, Damir Zaredinov, Marina Li, Bekhzod Talipov, Anna Belousova, Christian Kunze and Petra Schneider

Appl. Sci. 2021, 11(9), 3768; https://doi.org/10.3390/app11093768 - 22 Apr 2021

Cited by 10 | Viewed by 2471

Abstract

Central Asia is one of the most challenged places, prone to suffering from various natural hazards, where seismically triggered landslides have caused severe secondary losses. Research on this problem is especially important in the cross-border Mailuu-Suu catchment in Kyrgyzstan, since it is burdened by radioactive legacy sites and frequently affected by earthquakes and landslides. To identify the landslide-prone areas and to quantify the volume of landslide (VOL), Scoops3D was selected to evaluate the slope stability throughout a digital landscape in the Mailuu-Suu catchment. By performing the limit equilibrium analysis, both of landslide susceptibility index (LSI) and VOL were estimated under five earthquake scenarios. The results show that the upstream areas were more seismically vulnerable than the downstream areas. The susceptibility level rose significantly with the increase in earthquake strength, whereas the VOL was significantly higher under the extreme earthquake scenario than under the other four scenarios. After splitting the environmental variables into sub-classes, the spatial variations of LSI and VOL became more clear: the LSI reduced with the increase in elevation, slope, annual precipitation, and distances to faults, roads, and streams, whereas the highest VOL was observed in the areas with moderate elevations, high precipitation, grasslands, and mosaic vegetation. The relative importance analysis indicated that the explanatory power reduced with the increase in earthquake level and it was significant higher for LSI than for VOL. Among nine environmental variables, the distance to faults, annual precipitation, slope, and elevation were identified as important triggers of landslides. By a simultaneous assessment of both LSI and VOL and the identification of important triggers, the proposed modelling approaches can support local decision-makers and householders to identify landslide-prone areas, further design proper landslide hazard and risk management plans and, consequently, contribute to the resolution of transboundary pollution conflicts. Full article

(This article belongs to the Special Issue Assessment of Landslide Susceptibility and Hazard in the Big Data Era)

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18 pages, 28195 KiB

Open AccessArticle

Deformation Detection in Cyclic Landslides Prior to Their Reactivation Using Two-Pass Satellite Interferometry

by Pierpaolo Ciuffi, Benedikt Bayer, Matteo Berti, Silvia Franceschini and Alessandro Simoni

Appl. Sci. 2021, 11(7), 3156; https://doi.org/10.3390/app11073156 - 01 Apr 2021

Cited by 6 | Viewed by 2122

Abstract

Landslides are widespread geological features in Italy’s Northern Apennines, with slow-moving earthflows among the most common types. They develop in fine-grained rocks and are subject to periodic rainfall-induced reactivations alternating to phases of dormancy. In this paper, we use radar interferometry (InSAR) and information about landslide activity to investigate deformation signals on an areal basis and to assess the dynamics of recently reactivated earthflows. We use traditional two-pass interferometry by taking advantage of the short revisit time of the Sentinel 1 satellite to characterize 4 years of slope deformations over the 60 km² study area, where 186 landslides are mapped. Our results show that most intense and sustained deformation signals are associated with phenomena on the verge of reactivation, indicating that radar interferometry may have a potential for early warning purposes. By focusing on three specific earthflow reactivations, we analyze their dynamics through the years that preceded their failure. Despite inherent uncertainties, it was possible to retrieve the deformation signal’s temporal evolution, which displayed seasonally recurring accelerations, peaking during the major precipitation episodes in the area. Full article

(This article belongs to the Special Issue Assessment of Landslide Susceptibility and Hazard in the Big Data Era)

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17 pages, 7389 KiB

Open AccessArticle

Uncertainty Reduction of Unlabeled Features in Landslide Inventory Using Machine Learning t-SNE Clustering and Data Mining Apriori Association Rule Algorithms

by Omar F. Althuwaynee, Ali Aydda, In-Tak Hwang, Yoon-Kyung Lee, Sang-Wan Kim, Hyuck-Jin Park, Moon-Se Lee and Yura Park

Appl. Sci. 2021, 11(2), 556; https://doi.org/10.3390/app11020556 - 08 Jan 2021

Cited by 18 | Viewed by 3092

Abstract

A landslide inventory, after an intense rainfall event in 1998, Southwestern Korea, was collected by digitizing aerial photographs. This left high uncertainty in the inventoried features to be verified by ground truths. To reduce the uncertainty, the photographs were reexamined, supported by the time slider in Google Earth. We observed 77 deformed slopes, which were similar in shape and texture, to the inventoried landslides. We then sought to label the observed formations based on their spatial relationship with surrounding conditions. A three-phase methodology was developed. First, an inventory of landslide, no landslide, vulnerable slopes, and unlabeled features was analyzed based on spatial cluster patterns, and then the dimension was reduced using the t-distributed stochastic neighbor embedding (t-SNE). Second, the Apriori algorithm, based on association rule mining, was used to identify common relations in the inventory using landslide antecedent factors (derived from topographic and landcover maps) that are linked to areas of unlabeled features. Third, the findings were validated using Landsat TM (Thematic mapper) and ETM+(Enhanced thematic mapper) images acquired before and after the original inventory. Current research offers practical and economical solutions (reduced reliance on paid remote sensing sensors and field survey) to labeling and classification of missing or outdated spatial attributed information. Full article

(This article belongs to the Special Issue Assessment of Landslide Susceptibility and Hazard in the Big Data Era)

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23 pages, 7506 KiB

Open AccessArticle

Comparison of Approaches for Data Analysis of Multi-Parametric Monitoring Systems: Insights from the Acuto Test-Site (Central Italy)

by Matteo Fiorucci, Salvatore Martino, Francesca Bozzano and Alberto Prestininzi

Appl. Sci. 2020, 10(21), 7658; https://doi.org/10.3390/app10217658 - 29 Oct 2020

Cited by 9 | Viewed by 1823

Abstract

This paper deals with monitoring systems to manage the risk due to fast slope failures that involve rock masses, in which important elements (such as infrastructures or cultural heritages, among the others) are exposed. Three different approaches for data analysis were here compared to evaluate their suitability for detecting mutual relations among destabilising factors, acting on different time windows, and induced strain effects on rock masses: (i) an observation-based approach (OBA), (ii) a statistics-based approach (SBA) and (iii) a semi-empirical approach (SEA). For these purposes, a test-site has been realised in an abandoned quarry in Central Italy by installing a multi-parametric monitoring sensor network on a rock wall able to record strain effects induced by natural and anthropic forcing actions (like as temperature, rainfall, wind and anthropic vibrations). The comparison points out that the considered approaches allow one to identify forcing actions, responsible for the strain effects on the rock mass over several time windows, regarding a specific size (i.e., rock block dimensional scale). The OBA was more suitable for computing the relations over short- to medium time windows, as well as the role of impulsive actions (i.e., hourly to seasonal and/or instantaneous). The SBA was suitable for computing the relations over medium- to long time windows (i.e., daily to seasonal), also returning the time lag between forcing actions and induced strains using the cross-correlation statistical function. Last, the SEA was highly suitable for detecting irreversible strain effects over long- to very long-time windows (i.e., plurennial). Full article

(This article belongs to the Special Issue Assessment of Landslide Susceptibility and Hazard in the Big Data Era)

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22 pages, 10493 KiB

Open AccessArticle

Simulation of Debris-Flow Runout Near a Construction Site in Korea

by Byung-Gon Chae, Ying-Hsin Wu, Ko-Fei Liu, Junghae Choi and Hyuck-Jin Park

Appl. Sci. 2020, 10(17), 6079; https://doi.org/10.3390/app10176079 - 02 Sep 2020

Cited by 4 | Viewed by 2188

Abstract

This study analyzed landslide susceptibility and numerically simulated the runout distance of debris flows near a construction site in Korea. Landslide susceptibility was based on a landslide prediction map of the study area. In the prediction map, 3.5% of the area had a 70–90% landslide probability, while 0.79% had over 90% probability. Based on the landslide susceptibility analysis, debris flows in four watersheds were simulated to assess possible damage to the construction site. According to the simulations, debris flow in Watershed C approaches to within 9.6 m of the site. Therefore, the construction site could be impacted by debris flow in Watershed C. Although the simulated flows in Watersheds A and D do not directly influence the construction site, they could damage the nearby road and other facilities. The simulations also show that debris runout distance is strongly influenced by the volume of debris in the on-slope source area and by the slope angles along the debris-flow path. Full article

(This article belongs to the Special Issue Assessment of Landslide Susceptibility and Hazard in the Big Data Era)

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18 pages, 7650 KiB

Open AccessArticle

Determination of the Stability of High-Steep Slopes by Global Navigation Satellite System (GNSS) Real-Time Monitoring in Long Wall Mining

by Xugang Lian, Zoujun Li, Hongyan Yuan, Haifeng Hu, Yinfei Cai and Xiaoyu Liu

Appl. Sci. 2020, 10(6), 1952; https://doi.org/10.3390/app10061952 - 12 Mar 2020

Cited by 18 | Viewed by 2557

Abstract

Surface movement and deformation induced by underground coal mining causes slopes to collapse. Global Navigation Satellite System (GNSS) real-time monitoring can provide early warnings and prevent disasters. A stability analysis of high-steep slopes was conducted in a long wall mine in China, and a GNSS real-time monitoring system was established. The moving velocity and displacement at the monitoring points were an integrated response to the influencing factors of mining, topography, and rainfall. Underground mining provided a continuous external driving force for slope movement, the steep terrain provided sufficient slip conditions in the slope direction, and rainfall had an acceleration effect on slope movement. The non-uniform deformation, displacement field, and time series images of the slope body revealed that ground failure was concentrated in the area of non-uniform deformation. The non-uniform deformation was concentrated ahead of the working face, the speed of deformation behind the working face was reduced, the instability of the slope body was increased, and the movement of the top of the slope was larger than at the foot. The high-steep slope stability in the mine was influenced by the starting deformation (low stability), iso-accelerated deformation (increased stability), deformation deceleration (reduced stability), and deformation remaining unchanged (improved stability). Full article

(This article belongs to the Special Issue Assessment of Landslide Susceptibility and Hazard in the Big Data Era)

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21 pages, 8092 KiB

Open AccessArticle

Spatial Prediction of Landslides Using Hybrid Integration of Artificial Intelligence Algorithms with Frequency Ratio and Index of Entropy in Nanzheng County, China

by Wei Chen, Limin Fan, Cheng Li and Binh Thai Pham

Appl. Sci. 2020, 10(1), 29; https://doi.org/10.3390/app10010029 - 19 Dec 2019

Cited by 46 | Viewed by 3159

Abstract

The main object of this study is to introduce hybrid integration approaches that consist of state-of-the-art artificial intelligence algorithms (SysFor) and two bivariate models, namely the frequency ratio (FR) and index of entropy (IoE), to carry out landslide spatial prediction research. Hybrid integration approaches of these two bivariate models and logistic regression (LR) were used as benchmark models. Nanzheng County was considered as the study area. First, a landslide distribution map was produced using news reports, interpreting satellite images and a regional survey. A total of 202 landslides were identified and marked. According to the previous studies and local geological environment conditions, 16 landslide conditioning factors were chosen for landslide spatial prediction research: elevation, profile curvature, plan curvature, slope angle, slope aspect, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), distance to roads, distance to rivers, distance to faults, lithology, rainfall, soil, normalized different vegetation index (NDVI), and land use. Then, the 202 landslides were randomly segmented into two parts with a ratio of 70:30. Seventy percent of the landslides (141) were used as the training dataset and the remaining landslides (61) were used as the validating dataset. Next, the evaluation models were built using the training dataset and compared by the receiver operating characteristics (ROC) curve. The results showed that all models performed well; the FR_SysFor model exhibited the best prediction ability (0.831), followed by the IoE_SysFor model (0.819), IoE_LR model (0.702), FR_LR model (0.696), IoE model (0.691), and FR model (0.681). Overall, these six models are practical tools for landslide spatial prediction research and the results can provide a reference for landslide prevention and control in the study area. Full article

(This article belongs to the Special Issue Assessment of Landslide Susceptibility and Hazard in the Big Data Era)

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