Land | Special Issue : Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring

Research

21 pages, 6252 KiB

Open AccessArticle

Methodology and Results of Staged UAS Photogrammetric Rockslide Monitoring in the Alpine Terrain in High Tatras, Slovakia, after the Hydrological Event in 2022

by Ľudovít Kovanič, Martin Štroner, Rudolf Urban and Peter Blišťan

Land 2023, 12(5), 977; https://doi.org/10.3390/land12050977 - 28 Apr 2023

Cited by 5 | Viewed by 1552

Abstract

There are numerous talus cones that have formed by long-term geological processes and sudden hydrological events in the Small Cold Valley (High Tatras National Park in Slovakia). Frequent hiking trails lead here; therefore, their safeness needs to be monitored due to recent rock [...] Read more.

There are numerous talus cones that have formed by long-term geological processes and sudden hydrological events in the Small Cold Valley (High Tatras National Park in Slovakia). Frequent hiking trails lead here; therefore, their safeness needs to be monitored due to recent rock avalanches and landslides. A complex methodology for monitoring changes in talus cones was developed to determine the extent, pace, nature, and origin of the morphological changes in the land in this complex high-mountain terrain. Non-contact UAS photogrammetry with SfM-MVS processing was applied as a quick, reliable, and environment-friendly data acquisition method. For proper georeferencing, a network of GCPs and stabilized surveying points were established by terrestrial geodetic surveying. Together with an evaluation of the methodology, the results comparing the actual state of a talus cone in 2018 and 2022 (after the significant hydrological event) are presented. Comparing and analyzing spatial models represented by point clouds, with an accuracy of centimeter level, was obtained. The detected morphological changes reached values in meters. A differential model expresses the distribution of the morphological changes. In conclusion, geodetic and geological knowledge is synthesized to evaluate the phenomena occurring in this territory. Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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21 pages, 8044 KiB

Open AccessArticle

Impact of Factors That Predict Adoption of Geomonitoring Systems for Landslide Management

by Adrian T. Rădulescu, Corina M. Rădulescu, Nataliya Kablak, Oleksandr K. Reity and Gheorghe M. T. Rădulescu

Land 2023, 12(4), 752; https://doi.org/10.3390/land12040752 - 27 Mar 2023

Cited by 2 | Viewed by 1668

Abstract

Monitoring hazardous phenomena is a fundamental requirement of disaster risk management. Using new geomatic technologies integrated into complex geo information systems represents an innovative method of strengthening collaboration between stakeholder groups that aim at reducing the risk of disasters. This paper aimed to [...] Read more.

Monitoring hazardous phenomena is a fundamental requirement of disaster risk management. Using new geomatic technologies integrated into complex geo information systems represents an innovative method of strengthening collaboration between stakeholder groups that aim at reducing the risk of disasters. This paper aimed to investigate the factors of adapting a geomonitoring information system for landslides in the cross-border area of Hungary, Slovakia, Ukraine, and Romania; the analysis was carried out in the case of the cross-border project, GeoSES. This study developed and empirically tested a novel UTAUT model based on the unified theory of acceptance and use of technology. PL-SEM (partial least-squares structural equation modeling) was used to test the model’s hypotheses. The data were collected by employing an online questionnaire on a target group of beneficiaries of the GeoSES project, in which the geomonitoring information system was proposed as an innovative solution to landslide management and disaster risk reduction. This study’s importance and added value reside in the theoretical and practical implications of the proposed model for predicting the beneficiaries’ adaptation of the landslide monitoring system. The results have shown that the GeoSES project beneficiaries coming from four neighboring nations are willing to utilize the integrated monitoring systems, which is one of the strengths of the collaborative efforts focused on mitigating risks and managing disasters in this region. Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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18 pages, 9489 KiB

Open AccessArticle

Exploring the Application of a Debris Flow Likelihood Regression Model in Mediterranean Post-Fire Environments, Using Field Observations-Based Validation

by Michalis Diakakis, Spyridon Mavroulis, Emmanuel Vassilakis and Vassiliki Chalvatzi

Land 2023, 12(3), 555; https://doi.org/10.3390/land12030555 - 24 Feb 2023

Cited by 5 | Viewed by 1866

Abstract

Post-fire geomorphic processes and associated risks are an important threat in Mediterranean environments. Currently, post-fire mass movement prediction has limited applications across the Mediterranean despite the abundance of both forest fires and landslide/debris flow disasters. This work applies a debris flow generation likelihood [...] Read more.

Post-fire geomorphic processes and associated risks are an important threat in Mediterranean environments. Currently, post-fire mass movement prediction has limited applications across the Mediterranean despite the abundance of both forest fires and landslide/debris flow disasters. This work applies a debris flow generation likelihood model to evaluate the probability of mass movement phenomena in different catchments of a burnt area, after a catastrophic fire near Schinos (Attica, Greece) in 2021. Then, it uses field observations from the area, recording mass movement phenomena after high-intensity rainfall events, to validate the results. The findings show that the model is successful in determining the probability of debris flow generation in the 21 basins of the study area, ranging from 0.05 to 0.893. The probability values show a statistically significant correlation (sig. = 0.001) with the actual debris flow occurrences in the area, and satisfactory results in terms of the model’s predictive ability, functioning well within the particular geo-environmental characteristics of the Mediterranean environment. The results establish the reliability of the approach as a tool to assess mass movement risks in a region with an abundance of post-fire related hazards and disastrous events. Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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27 pages, 6990 KiB

Open AccessArticle

A GIS-Based Kinematic Analysis for Jointed Rock Slope Stability: An Application to Himalayan Slopes

by Jagadish Kundu, Kripamoy Sarkar, Ebrahim Ghaderpour, Gabriele Scarascia Mugnozza and Paolo Mazzanti

Land 2023, 12(2), 402; https://doi.org/10.3390/land12020402 - 2 Feb 2023

Cited by 7 | Viewed by 3366

Abstract

GIS-based kinematic stability analysis in rock slopes is a rare practice in geological engineering despite its immense potential to delineate unstable zones in a mountainous region. In this article, we have used a GIS-based modified technique to assess the efficiency of kinematic analysis [...] Read more.

GIS-based kinematic stability analysis in rock slopes is a rare practice in geological engineering despite its immense potential to delineate unstable zones in a mountainous region. In this article, we have used a GIS-based modified technique to assess the efficiency of kinematic analysis in predicting shallow landslides in the rock slopes of the Himalayan mountains on a regional scale. The limited use of this technique is primarily due to the complexities involved in its practical application. To make this technique more effective and convenient usability, we present modified methods and a new application, ‘GISMR’, that works with the aid of GIS software for the determination of kinematic susceptibility. A modified kinematic analysis method was implemented to define the stability in terms of failure susceptibility on a scale of 0 to 100 rather than a conservative result, such as failure or non-failure. We also present another functionality of the GISMR that provides optimised slope angles over a region. This functionality could aid the decision-making process when selecting a suitable location for a road path or other engineering constructions that are impacted by unstable mountain slopes. The applicability of this new method was demonstrated in a rock failure-prone region in the mountains of the Indian Himalayas. The outcomes delineate the unstable slopes in the region, which are intersected by a strategic National Highway 05 and have a long history of landslide-related hazards. It was found that 9.61% of the area is susceptible to failure. However, 2.28% is classified as a low susceptible region, and 2.58% of the area is very-low susceptible. The regions with moderately high, high, and very-high susceptibility cover 2.78%, 1.49%, and 0.46% of the whole area, respectively. The results were evaluated by receiver operating characteristic curve and a frequency ratio method to represent the association between kinematic susceptibility and the mass movement inventory in the area. It is concluded that kinematic susceptibility has a strong relationship with landslide activity in the rock slopes of the Himalayan region. Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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14 pages, 2946 KiB

Open AccessArticle

The Method of Segmenting the Early Warning Thresholds Based on Fisher Optimal Segmentation

by Xiangyu Li, Tianjie Lei, Jing Qin, Jiabao Wang, Weiwei Wang, Baoyin Liu, Dongpan Chen, Guansheng Qian, Li Zhang and Jingxuan Lu

Land 2023, 12(2), 344; https://doi.org/10.3390/land12020344 - 27 Jan 2023

Viewed by 1390

Abstract

Most slope collapse accidents are indicated by certain signs before their occurrence, and unnecessary losses can be avoided by predicting slope deformation. However, the early warning signs of slope deformation are often misjudged. It is necessary to establish a method to determine the [...] Read more.

Most slope collapse accidents are indicated by certain signs before their occurrence, and unnecessary losses can be avoided by predicting slope deformation. However, the early warning signs of slope deformation are often misjudged. It is necessary to establish a method to determine the appropriate early warning signs in sliding thresholds. Here, to better understand the impact of different scales on the early warning signs of sliding thresholds, we used the Fisher optimal segmentation method to establish the early warning signs of a sliding threshold model based on deformation speed and deformation acceleration at different spatial scales. Our results indicated that the accuracy of the early warning signs of sliding thresholds at the surface scale was the highest. Among them, the early warning thresholds of the blue, yellow, orange, and red level on a small scale were 369.31 mm, 428.96 mm, 448.41 mm, and 923.7 mm, respectively. The evaluation accuracy of disaster non-occurrence and occurrence was 93.25% and 92.41%, respectively. The early warning thresholds of the blue, yellow, orange, and red level on a large scale were 980.11 mm, 1038.16 mm, 2164.63 mm, and 9492.75 mm, respectively. The evaluation accuracy of disaster non-occurrence and occurrence was 97.22% and 97.44%, respectively. Therefore, it is necessary to choose deformation at the surface scale with a large scale as the sliding threshold. Our results effectively solve the problem of misjudgment of the early warning signs of slope collapse, which is of great significance for ensuring the safe operation of water conservation projects and improving the slope deformation warning capability. Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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25 pages, 17217 KiB

Open AccessArticle

Landslide Susceptibility Mapping under the Climate Change Impact in the Chania Regional Unit, West Crete, Greece

by Constantinos Nefros, Dimitrios S. Tsagkas, Gianna Kitsara, Constantinos Loupasakis and Christos Giannakopoulos

Land 2023, 12(1), 154; https://doi.org/10.3390/land12010154 - 3 Jan 2023

Cited by 7 | Viewed by 2869

Abstract

Over the preceding decades, climate change has affected precipitation, the most common factor triggering landslides. The aim of this study is to highlight this impact by examining the precipitation trends in the Chania regional unit, Greece, with the help of the precipitation time [...] Read more.

Over the preceding decades, climate change has affected precipitation, the most common factor triggering landslides. The aim of this study is to highlight this impact by examining the precipitation trends in the Chania regional unit, Greece, with the help of the precipitation time series provided by 21 local meteorological stations covering a period from 1955 to 2020. The analysis also focuses on the extreme precipitation events of February 2019, where the monthly cumulated precipitation amount reached 1225 mm, one of the highest ever recorded in Greece. Moreover, an inventory of past and recent landslides was created and the intensity–duration landslide precipitation thresholds were evaluated. Daily simulations of precipitation from three state-of-the-art regional climate models were used to analyze precipitation patterns under two representative concentration pathways (RCPs), 4.5 and 8.5, for the period 2030–2060. The application of the estimated precipitation thresholds on the daily future precipitation projections revealed an increase in the following decades of the precipitation events that can activate a landslide and, therefore, highlighted the climate change impact. Moreover, the mean annual precipitation of the preceding 10 years was evaluated and used along with local hydro-geological data and the recent landslide inventory, providing approximately a 5% more effective landslide susceptibility map compared with the relative maps produced by using the mean annual precipitation evaluated for the control period (1976–2005) and for the preceding 30 years. Thus, landslide susceptibility emerges as a dynamic process and the landslide susceptibility map needs to be regularly updated due to the significant and ongoing changes in precipitation because of climate change. Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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17 pages, 14294 KiB

Open AccessArticle

Inventory and Spatial Distribution of Ancient Landslides in Hualong County, China

by Yuandong Huang, Chong Xu, Lei Li, Xiangli He, Jia Cheng, Xiwei Xu, Junlei Li and Xujiao Zhang

Land 2023, 12(1), 136; https://doi.org/10.3390/land12010136 - 31 Dec 2022

Cited by 11 | Viewed by 2307

Abstract

The establishment of a regional historical landslide inventory plays an indispensable role in landslide assessment and prevention. In this study, based on the Google Earth platform, an inventory of ancient landslides in Hualong County, Qinghai Province was established. The inventory includes 3517 ancient [...] Read more.

The establishment of a regional historical landslide inventory plays an indispensable role in landslide assessment and prevention. In this study, based on the Google Earth platform, an inventory of ancient landslides in Hualong County, Qinghai Province was established. The inventory includes 3517 ancient landslides with individual areas ranging from 2354.6 m² to 12.44 km². The dominant characteristics include an elevation of 2600–2800 m, slope of 10–20°, aspects SW, W, and NW, mudstone and sandstone of Paleoproterozoic, Neoproterozoic and Quaternary loess, 8–10 km from faults, 0–1 km from rivers, cultivated and grassland types, NDVI of 0.25–0.3, and an average precipitation in the range of 480–500 mm. In addition, the geometric analysis of landslides shows that the average height and length of ancient landslides in the study area are 151.92 m and 429.52 m, respectively. The power law relationship between the two is L = 0.41 × H^1.37. The ancient landslide inventory of this study exhibits an integrated pattern of the development characteristics and spatial distribution of landslides in the Tibetan Plateau and the upper Yellow River basin, as well as providing a significant reference for subsequent landslide susceptibility mapping in the area. Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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21 pages, 2380 KiB

Open AccessArticle

Two-Phase Two-Layer Depth-Integrated SPH-FD Model: Application to Lahars and Debris Flows

by Saeid Moussavi Tayyebi, Manuel Pastor, Andrei Hernandez, Lingang Gao, Miguel Martin Stickle, Ashenafi Lulseged Yifru and Vikas Thakur

Land 2022, 11(10), 1629; https://doi.org/10.3390/land11101629 - 22 Sep 2022

Cited by 3 | Viewed by 2216

Abstract

The complex nature of debris flows suggests that the pore-water pressure evolution and dewatering of a flowing mass caused by the high permeability of soil or terrain could play an essential role in the dynamics behavior of fast landslides. Dewatering causes desaturation, reducing [...] Read more.

The complex nature of debris flows suggests that the pore-water pressure evolution and dewatering of a flowing mass caused by the high permeability of soil or terrain could play an essential role in the dynamics behavior of fast landslides. Dewatering causes desaturation, reducing the pore-water pressure and improving the shear strength of liquefied soils. A new approach to landslide propagation modeling considering the dewatering of a mass debris flow has drawn research attention. The problem is characterized by a transition from saturated to unsaturated soil. This paper aims to address this scientific gap. A depth-integrated model was developed to analyze the dewatering of landslides, in which, desaturation plays an important role in the dynamics behavior of the propagation. This study adopted an SPH numerical method to model landslide propagation consisting of pore-water and a soil skeleton in fully or partially saturated soils. In a two-phase model, the soil–water mixture was discretized and represented by two sets of SPH nodes carrying all field variables, such as velocity, displacement, and basal pore-water pressure. The pore-water was described by an additional set of balance equations to take into account its velocity. In the developed two-layer model, an upper desaturated layer and a lower saturated layer were considered to enhance the description of dewatering. This is the so-called two-phase two-layer formulation, which is capable of simulating the entire process of landslides propagation, including the large deformation of soils and corresponding pore-water pressure evolutions, where the effect of the dewatering in saturated soils is also taken into account. A dam-break problem was analyzed through the new and previously developed model. A flume test performed at Trondheim was also used to validate the proposed model by comparing the numerical results with measurements obtained from the experiment. Finally, the model was applied to simulate a real case lahar, which is an appropriate benchmark case used to examine the applicability of the developed model. The simulation results demonstrated that taking into account the effects of dewatering and the vital parameter of relative height is essential for the landslide propagation modeling of a desaturated flowing mass. Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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16 pages, 2182 KiB

Open AccessArticle

A Research on Cohesion Hyperspectral Detection Model of Fine-Grained Sediments in Beichuan Debris Flow, Sichuan Province, China

by Qinjun Wang, Jingjing Xie, Jingyi Yang, Peng Liu, Dingkun Chang and Wentao Xu

Land 2022, 11(9), 1609; https://doi.org/10.3390/land11091609 - 19 Sep 2022

Cited by 5 | Viewed by 1556

Abstract

Cohesion is the main inter-controlled factor for the stability of fine-grained sediments in debris flow, and plays an important role in debris flow hazard early warning. At present, there is no cohesion rapid remote sensing detection model, which seriously affects the development of [...] Read more.

Cohesion is the main inter-controlled factor for the stability of fine-grained sediments in debris flow, and plays an important role in debris flow hazard early warning. At present, there is no cohesion rapid remote sensing detection model, which seriously affects the development of quantitative evaluation on debris flow stability. How to use remote sensing to quickly detect the cohesion of fine-grained debris has become an important scientific issue. Therefore, strengthening the research on the cohesion hyperspectral detection model, indicating its sensitive spectral bands, and establishing a quantitative model between cohesion and these bands are of great significance not only in discovering the stability mechanism, but also in quickly establishing the stability detection model for gully sediments. Taking the Beichuan debris flow as the study area, we carried out experiments on cohesion, cohesion influencing factors, and spectra. Firstly, six cohesion hyperspectral sensitive bands are indicated in red, near infrared portions of the electromagnetic spectrum, including 750, 1578, 1835, 2301, 2305, and 2309 nm; secondly, these bands discover the cohesion influencing factors. Band 750 nm indicates the characteristics of cohesion, effective internal friction angle, and permeability coefficient, while the other five bands indicate the characteristics of effective internal friction angle, density, and moisture; finally, a hyperspectral remote sensing detection model for the fine-grained sediments cohesion is established. With a correlation coefficient of 0.56, and p value less than 0.001, the model indicates that cohesion has a great significant correlation with the six bands. This not only provides sensitive bands for detecting cohesion of fine-grained sediments using remote sensing, but also provides a scientific basis for rapid detection of the fine-grained sediments’ stability in large areas. Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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37 pages, 11382 KiB

Open AccessArticle

The Use of High-Resolution Remote Sensing Data in Preparation of Input Data for Large-Scale Landslide Hazard Assessments

by Marko Sinčić, Sanja Bernat Gazibara, Martin Krkač, Hrvoje Lukačić and Snježana Mihalić Arbanas

Land 2022, 11(8), 1360; https://doi.org/10.3390/land11081360 - 21 Aug 2022

Cited by 7 | Viewed by 2376

Abstract

The objective of the study is to show that landslide conditioning factors derived from different source data give significantly different relative influences on the weight factors derived with statistical models for landslide susceptibility modelling and risk analysis. The analysis of the input data [...] Read more.

The objective of the study is to show that landslide conditioning factors derived from different source data give significantly different relative influences on the weight factors derived with statistical models for landslide susceptibility modelling and risk analysis. The analysis of the input data for large-scale landslide hazard assessment was performed on a study area (20.2 km²) in Hrvatsko Zagorje (Croatia, Europe), an area highly susceptible to sliding with limited geoinformation data, including landslide data. The main advantage of remote sensing technique (i.e., LiDAR, Light Detection and Ranging) data and orthophoto images is that they enable 3D surface models with high precision and spatial resolution that can be used for deriving all input data needed for landslide hazard assessment. The visual interpretation of LiDAR DTM (Digital Terrain Model) morphometric derivatives resulted in a detailed and complete landslide inventory map, which consists of 912 identified and mapped landslides, ranging in size from 3.3 to 13,779 m². This inventory was used for quantitative analysis of 16 input data layers from 11 different sources to analyse landslide presence in factor classes and thus comparing landslide conditioning factors from available small-scale data with high-resolution LiDAR data and orthophoto images, pointing out the negative influence of small-scale source data. Therefore, it can be concluded that small-scale landslide factor maps derived from publicly available sources should not be used for large-scale analyses because they will result in incorrect assumptions about conditioning factors compared with LiDAR DTM derivative factor maps. Furthermore, high-resolution LiDAR DTM and orthophoto images are optimal input data because they enable derivation of the most commonly used landslide conditioning factors for susceptibility modelling and detailed datasets about elements at risk (i.e., buildings and traffic infrastructure data layers). Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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19 pages, 3116 KiB

Open AccessArticle

Large Shear Strength Parameters for Landslide Analyses on Highly Weathered Flysch

by Sofia Anagnostopoulou, Nikolaos Depountis, Nikolaos Sabatakakis and Panagiotis Pelekis

Land 2022, 11(8), 1353; https://doi.org/10.3390/land11081353 - 19 Aug 2022

Viewed by 2128

Abstract

Many significant landslide movements are often observed in the upper weathering zone of flysch, which constitutes the most critical landslide-prone geological formation in Western Greece. In this article, a laboratory approach is adopted to investigate the behavior of highly weathered and tectonically decomposed [...] Read more.

Many significant landslide movements are often observed in the upper weathering zone of flysch, which constitutes the most critical landslide-prone geological formation in Western Greece. In this article, a laboratory approach is adopted to investigate the behavior of highly weathered and tectonically decomposed flysch for slope stability analyses with the performance of large shear testing in reconstituted soil specimens. The testing program included several reconstituted flysch specimens derived from three representative landslides. Tests under large direct shearing (300 × 300 × 120 mm) were conducted in moisture- and density-controlled conditions and ring shear tests were conducted in the finer material. The test results revealed that the values of the effective angle of friction in the flysch material decrease with the increasing water content. Moreover, dense specimens showed curved failure envelopes due to dilatancy, especially in dry conditions. A comparison of laboratory test results with those obtained by performing back-analyses under saturated conditions has shown that the sliding of the weathered and decomposed flysch mainly depends on its residual angle of friction which was found to be 1°–6° lower than the ultimate angle of friction as it was estimated by the large shear tests. Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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13 pages, 3995 KiB

Open AccessArticle

Impact of an Uncertain Structural Constraint on Electrical Resistivity Tomography for Water Content Estimation in Landslides

by Jasmin Grifka, Maximilian Weigand, Andreas Kemna and Thomas Heinze

Land 2022, 11(8), 1207; https://doi.org/10.3390/land11081207 - 31 Jul 2022

Cited by 3 | Viewed by 1580

Abstract

Geoelectrical methods can be part of early warning systems for landslide-prone hillslopes by giving estimates of the water content distribution. Structurally constrained inversions of geoelectrical data can improve the water content estimation by reducing the smoothness constraint along known lithological boundaries, which is [...] Read more.

Geoelectrical methods can be part of early warning systems for landslide-prone hillslopes by giving estimates of the water content distribution. Structurally constrained inversions of geoelectrical data can improve the water content estimation by reducing the smoothness constraint along known lithological boundaries, which is especially important for landslides, as often layers with strongly divergent hydrological parameters and varying electrical signatures are present in landslides. However, any a priori information about those boundaries has an intrinsic uncertainty. A detailed synthetic study and a field investigation are combined to study the influence of misplaced structural constraints and the strength of the smoothness reduction via a coupling coefficient on inversion results of electrical resistivity data. While a well-known lithological boundary with a substantial reduction of the smoothness constraint can significantly improve the inversion result, a flawed constraint can cause strong divergences from the synthetic model. The divergence can even grow above the divergence of a fully smoothed inversion result. For correctly placed structural constraints, a coupling coefficient smaller than

10^{- 4}

uncovers previously unseen dynamics in the resistivity distribution compared to smoothed inversion results. Uncertain layer boundaries can be included in the inversion process with a larger coupling coefficient to avoid flawed results as long as the uncertainty of the layer thickness is below 20%. The application to field data confirms these findings but is less sensitive to a further reduction of the coupling coefficient, probably due to uncertainties in the structural information. Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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26 pages, 4216 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

The Role of Soil Type in Triggering Shallow Landslides in the Alps (Lombardy, Northern Italy)

by Fabio Luino, Jerome De Graff, Marcella Biddoccu, Francesco Faccini, Michele Freppaz, Anna Roccati, Fabrizio Ungaro, Michele D’Amico and Laura Turconi

Land 2022, 11(8), 1125; https://doi.org/10.3390/land11081125 - 22 Jul 2022

Cited by 11 | Viewed by 4427

Abstract

Shallow landslides due to the soil saturation induced by intense rainfall events are very common in northern Italy, particularly in the Alps and Prealps. They are usually triggered during heavy rainstorms, causing severe damage to property, and sometimes causing casualties. A historical study [...] Read more.

Shallow landslides due to the soil saturation induced by intense rainfall events are very common in northern Italy, particularly in the Alps and Prealps. They are usually triggered during heavy rainstorms, causing severe damage to property, and sometimes causing casualties. A historical study and analysis of shallow landslides and mud-debris flows triggered by rainfall events in Lombardy was carried out for the period of 1911–2010, over an area of 14,019 km². In this study, intensity–duration rainfall thresholds have been defined using the frequentist approach, considering some pedological characteristics available in regional soil-related databases, such as the soil region, the textural class, and the dominant soil typological units (STU). The soil-based empirical rainfall thresholds obtained considering the soil regions of the study area were significantly different, with a lower threshold for landslide occurrence in the soil region M1 (Alps), where soils developed over siliceous parent material, with respect to the whole study area and the soil region M2 (Prealps), where soils developed over calcareous bedrocks. Furthermore, by considering textural classes, the curves were differentiated, with coarse-textured soils found more likely to triggerlandslides than fine soils. Finally, considering both texture and main soil groups, given the same rainfall duration, the rainfall amount and intensity needed to initiate a landslide increased in the following order: “coarse-skeletal” Cambisols < Umbrisols < Podzols < “fine” Cambisols. The results of this study highlighted the relevant role of pedological conditioning factors in differentiating the activation of rainfall-induced shallow landslides in a definite region. The information on soils can be used to define more precise rainfall–pedological thresholds than empirical thresholds based solely on meteorological conditions, even when they are locally defined. This knowledge is crucial for forecasting and preventing geo-hydrological processes and in developing better warning strategies to mitigate risks and to reduce socio-economic damage. Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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25 pages, 3254 KiB

Open AccessArticle

GIS-Based Comparative Study of the Bayesian Network, Decision Table, Radial Basis Function Network and Stochastic Gradient Descent for the Spatial Prediction of Landslide Susceptibility

by Junpeng Huang, Sixiang Ling, Xiyong Wu and Rui Deng

Land 2022, 11(3), 436; https://doi.org/10.3390/land11030436 - 17 Mar 2022

Cited by 28 | Viewed by 3753

Abstract

Landslides frequently occur along the eastern margin of the Tibetan Plateau, which poses a risk to the construction, maintenance, and transportation of the proposed Dujiangyan city to Siguniang Mountain (DS) railway, China. Therefore, four advanced machine learning models, namely, the Bayesian network (BN), [...] Read more.

Landslides frequently occur along the eastern margin of the Tibetan Plateau, which poses a risk to the construction, maintenance, and transportation of the proposed Dujiangyan city to Siguniang Mountain (DS) railway, China. Therefore, four advanced machine learning models, namely, the Bayesian network (BN), decision table (DTable), radial basis function network (RBFN), and stochastic gradient descent (SGD), are proposed in this study to delineate landslide susceptibility zones. First, a landslide inventory map was randomly divided into 828 (75%) samples and 276 (25%) samples for training and validation, respectively. Second, the One-R technique was utilized to analyze the importance of 14 variables. Then, the prediction capability of the four models was validated and compared in terms of different statistical indices (accuracy (ACC) and Cohen’s kappa coefficient (k)) and the areas under the curve (AUC) in the receiver operating characteristic curve. The results showed that the SGD model performed best (AUC = 0.897, ACC = 80.98%, and k = 0.62), followed by the BN (AUC = 0.863, ACC = 78.80%, and k = 0.58), RBFN (AUC = 0.846, ACC = 77.36%, and k = 0.55), and DTable (AUC = 0.843, ACC = 76.45%, and k = 0.53) models. The susceptibility maps revealed that the DS railway segments from Puyang town to Dengsheng village are in high and very high-susceptibility zones. Full article

(This article belongs to the Special Issue Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring)

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