Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring II

Topic Information

Dear Colleagues,

Landslides, debris flows, rock falls, rock avalanches, and lahars are gravitational processes affecting different-sized areas and operate at different speeds depending on the geological and geomorphological context (tectonic setting, lithology, terrain morphology, hydrology and hydrogeology). They represent a dynamic response to a set of triggering factors mainly heavy rainfall, seismicity, volcanism, and human activities. The risk they represent for human life and economic activity is increasing due to the constantly increasing population, land-use changes, and climate change. Their socioeconomic repercussions include the cost to individuals, local communities, national services, and industry.

Different approaches are available to analyze landslide scenarios in order to assess, mitigate, and manage the related risks: laboratory and field investigations, susceptibility mapping, physical and numerical modelling, monitoring techniques, early warning system design, and so on. This Topic focuses on i) recent enhancements and trends in data acquisition technologies and landslide monitoring techniques, such as the use of UAVs (unmanned aerial vehicles) for tracking and monitoring the movements of landslides or WSN (wireless sensor network) applications for real-time monitoring purposes, SFM (structure-from-motion) photogrammetry applications, and so on; and ii) studies devoted to physical and numerical modelling of landslides aiming to explore recent advances and future challenges.

Contributions may cover a broad range of topics ranging from remote sensing applications and susceptibility mapping to physical and numerical modelling, utilization of sensor technology in landslide monitoring, the Internet of Things (IoT) for landslide monitoring, machine learning, and deep learning. Reviews of the state of the art on the mentioned topics are also encouraged, as well as case studies on landslide risk management.

We look forward to receiving your contributions.

Dr. Irene Manzella
Dr. Bouchra Haddad
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
GeoHazards geohazards	-	2.6	2020	19 Days	CHF 1000
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600
Land land	3.2	4.9	2012	16.9 Days	CHF 2600

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Published Papers (5 papers)

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All GeoHazards Remote Sensing Sensors Land

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20 pages, 15846 KiB  

Open AccessArticle

Modelling the Control of Groundwater on the Development of Colliery Spoil Tip Failures in Wales

by Lingfeng He, John Coggan, Patrick Foster, Tikondane Phiri and Matthew Eyre

Land 2024, 13(8), 1311; https://doi.org/10.3390/land13081311 - 19 Aug 2024

Viewed by 1156

Abstract

Legacy colliery spoil tip failures pose a significant hazard that can result in harm to persons or damage to property and infrastructure. In this research, the 2020 Wattstown tip landslide caused by heavy rainfall was examined to investigate the likely mechanisms and developmental [...] Read more.

Legacy colliery spoil tip failures pose a significant hazard that can result in harm to persons or damage to property and infrastructure. In this research, the 2020 Wattstown tip landslide caused by heavy rainfall was examined to investigate the likely mechanisms and developmental factors contributing to colliery spoil tip failures in Welsh coalfields. To achieve this, an integrated method was proposed through the combination of remote sensing mapping, stability chart analysis, 2D limit equilibrium (LE) modelling, and 3D finite difference method (FDM) analysis. Various water table geometries were incorporated into these models to ascertain the specific groundwater condition that triggered the occurrence of the 2020 landslide. In addition, sensitivity analyses were carried out to assess the influence of the colliery spoil properties (i.e., cohesion, friction angle, and porosity) on the slope stability analysis. The results indicate that the landslide was characterised by a shallow rotational failure mode and spatially constrained by the critical water table and an underlying geological interface. In addition, the results also imply that the landslide was triggered by the rise of water table associated with heavy rainfall. Through sensitivity analysis, it was found that the properties of the colliery spoil played an important role in confining the extent of the landslide and controlling the process of its development. The findings underscore the detrimental effects of increased pore pressures, induced by heavy rainfall, on the stability of colliery tips, highlighting the urgent needs for local government to enhance water management strategies for this region. Full article

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

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22 pages, 12234 KiB  

Open AccessArticle

Machine Learning-Driven Landslide Susceptibility Mapping in the Himalayan China–Pakistan Economic Corridor Region

by Mohib Ullah, Bingzhe Tang, Wenchao Huangfu, Dongdong Yang, Yingdong Wei and Haijun Qiu

Land 2024, 13(7), 1011; https://doi.org/10.3390/land13071011 - 8 Jul 2024

Cited by 5 | Viewed by 1403

Abstract

The reliability of data-driven approaches in generating landslide susceptibility maps depends on data quality, analytical method selection, and sampling techniques. Selecting optimal datasets and determining the most effective analytical methods pose significant challenges. This study assesses the performance of seven machine learning classifiers [...] Read more.

The reliability of data-driven approaches in generating landslide susceptibility maps depends on data quality, analytical method selection, and sampling techniques. Selecting optimal datasets and determining the most effective analytical methods pose significant challenges. This study assesses the performance of seven machine learning classifiers in the Himalayan region of the China–Pakistan Economic Corridor, utilizing statistical techniques and validation metrics. Thirteen geo-environmental variables were analyzed, including topographic (8), land cover (1), hydrological (1), geological (2), and meteorological (1) factors. These variables were evaluated for multicollinearity, feature importance, and their influence on landslide incidences. Our findings indicate that Support Vector Machines and Logistic Regression were highly effective, particularly near fault zones and roads, due to their effectiveness in handling complex, non-linear terrain interactions. Conversely, Random Forest and Logistic Regression demonstrated variability in their results. Each model distinctly identified landslide susceptibility zones ranging from very low to very high risk. Significant conditioning variables such as elevation, rainfall, lithology, slope, and land use were identified, reflecting the unique geomorphological conditions of the Himalayas. Further analysis using the Variance Inflation Factor and Pearson correlation coefficient showed minimal multicollinearity among the variables. Moreover, evaluations of Area Under the Receiver Operating Characteristic Curve (AUC-ROC) values confirmed the strong predictive capabilities of the models, with the Random Forest Classifier performing exceptionally well, achieving an AUC of 0.96 and an F-Score of 0.86. This study shows the importance of model selection based on dataset characteristics to enhance decision-making and strategy effectiveness. Full article

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

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16 pages, 7776 KiB  

Open AccessArticle

Effect of Rockfall Spatial Representation on the Accuracy and Reliability of Susceptibility Models (The Case of the Haouz Dorsale Calcaire, Morocco)

by Youssef El Miloudi, Younes El Kharim, Ali Bounab and Rachid El Hamdouni

Land 2024, 13(2), 176; https://doi.org/10.3390/land13020176 - 2 Feb 2024

Cited by 2 | Viewed by 1377

Abstract

Rockfalls can cause loss of life and material damage. In Northern Morocco, rockfalls and rock avalanche-deposits are frequent, especially in the Dorsale Calcaire morpho-structural unit, which is mostly formed by Jurassic limestone and dolostone formations. In this study, we focus exclusively on its [...] Read more.

Rockfalls can cause loss of life and material damage. In Northern Morocco, rockfalls and rock avalanche-deposits are frequent, especially in the Dorsale Calcaire morpho-structural unit, which is mostly formed by Jurassic limestone and dolostone formations. In this study, we focus exclusively on its northern segment, conventionally known as “the Haouz subunit”. First, a rockfall inventory was conducted. Then, two datasets were prepared: one covering exclusively the source area and the other representing the entirety of the mass movements (source + propagation area). Two algorithms were then used to build rockfall susceptibility models (RSMs). The first one (Logistic Regression: LR) yielded the most unreliable results, where the RSM derived from the source area dataset significantly outperformed the one based on the entirety of the rockfall affected area, despite the lack of significant visual differences between both models. However, the RSMs produced using Artificial Neural Networks (ANNs) were more or less similar in terms of accuracy, despite the source area model being more conservative. This result is unexpected given the fact that previous studies proved the robustness of the LR algorithm and the sensitivity of ANN models. However, we believe that the non-linear correlation between the spatial distribution of the rockfall propagation area and that of the conditioning factors used to compute the models explains why modeling rockfalls in particular differs from other types of landslides. Full article

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

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22 pages, 10665 KiB  

Open AccessArticle

Design and Implementation of a Prototype Seismogeodetic System for Tectonic Monitoring

by Javier Ramírez-Zelaya, Belén Rosado, Vanessa Jiménez, Jorge Gárate, Luis Miguel Peci, Amós de Gil, Alejandro Pérez-Peña and Manuel Berrocoso

Sensors 2023, 23(21), 8986; https://doi.org/10.3390/s23218986 - 5 Nov 2023

Cited by 2 | Viewed by 2041

Abstract

This manuscript describes the design, development, and implementation of a prototype system based on seismogeodetic techniques, consisting of a low-cost MEMS seismometer/accelerometer, a biaxial inclinometer, a multi-frequency GNSS receiver, and a meteorological sensor, installed at the Doñana Biological Station (Huelva, Spain) that transmits [...] Read more.

This manuscript describes the design, development, and implementation of a prototype system based on seismogeodetic techniques, consisting of a low-cost MEMS seismometer/accelerometer, a biaxial inclinometer, a multi-frequency GNSS receiver, and a meteorological sensor, installed at the Doñana Biological Station (Huelva, Spain) that transmits multiparameter data in real and/or deferred time to the control center at the University of Cadiz. The main objective of this system is to know, detect, and monitor the tectonic activity in the Gulf of Cadiz region and adjacent areas in which important seismic events occur produced by the interaction of the Eurasian and African plates, in addition to the ability to integrate into a regional early warning system (EWS) to minimize the consequences of dangerous geological phenomena. Full article

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

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37 pages, 24846 KiB  

Open AccessArticle

Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation

by Deliang Sun, Danlu Chen, Jialan Zhang, Changlin Mi, Qingyu Gu and Haijia Wen

Land 2023, 12(5), 1018; https://doi.org/10.3390/land12051018 - 5 May 2023

Cited by 35 | Viewed by 5354

Abstract

(1) Background: The aim of this paper was to study landslide susceptibility mapping based on interpretable machine learning from the perspective of topography differentiation. (2) Methods: This paper selects three counties (Chengkou, Wushan and Wuxi counties) in northeastern Chongqing, delineated as the corrosion [...] Read more.

(1) Background: The aim of this paper was to study landslide susceptibility mapping based on interpretable machine learning from the perspective of topography differentiation. (2) Methods: This paper selects three counties (Chengkou, Wushan and Wuxi counties) in northeastern Chongqing, delineated as the corrosion layered high and middle mountain region (Zone I), and three counties (Wulong, Pengshui and Shizhu counties) in southeastern Chongqing, delineated as the middle mountainous region of strong karst gorges (Zone II), as the study area. This study used a Bayesian optimization algorithm to optimize the parameters of the LightGBM and XGBoost models and construct evaluation models for each of the two regions. The model with high accuracy was selected according to the accuracy of the evaluation indicators in order to establish the landslide susceptibility mapping. The SHAP algorithm was then used to explore the landslide formation mechanisms of different landforms from both a global and local perspective. (3) Results: The AUC values for the test set in the LightGBM mode for Zones I and II are 0.8525 and 0.8859, respectively, and those for the test set in the XGBoost model are 0.8214 and 0.8375, respectively. This shows that LightGBM has a high prediction accuracy with regard to both landforms. Under the two different landform types, the elevation, land use, incision depth, distance from road and the average annual rainfall were the common dominant factors contributing most to decision making at both sites; the distance from a fault and the distance from the river have different degrees of influence under different landform types. (4) Conclusions: the optimized LightGBM-SHAP model is suitable for the analysis of landslide susceptibility in two types of landscapes, namely the corrosion layered high and middle mountain region, and the middle mountainous region of strong karst gorges, and can be used to explore the internal decision-making mechanism of the model at both the global and local levels, which makes the landslide susceptibility prediction results more realistic and transparent. This is beneficial to the selection of a landslide susceptibility index system and the early prevention and control of landslide hazards, and can provide a reference for the prediction of potential landslide hazard-prone areas and interpretable machine learning research. Full article

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

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