Topic Editors

Department of Applied Earth Sciences (AES), Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7522 NH Enschede, The Netherlands
Department of Environmental and Civil Engineering (Département Génie Civil Environnemental), Université de Bordeaux, 33400 Bordeaux, France

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

Abstract submission deadline
closed (31 December 2023)
Manuscript submission deadline
31 March 2024
Viewed by
4082

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

  • landslides
  • data acquisition
  • GIS, remote sensing and machine learning
  • susceptibility mapping
  • physical and numerical modelling
  • monitoring techniques
  • early warning system

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
GeoHazards
geohazards
- - 2020 20.7 Days CHF 1000 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit
Sensors
sensors
3.9 6.8 2001 17 Days CHF 2600 Submit
Land
land
3.9 3.7 2012 14.8 Days CHF 2600 Submit

Preprints.org is a multidiscipline platform providing preprint service that is dedicated to sharing your research from the start and empowering your research journey.

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

  1. Immediately share your ideas ahead of publication and establish your research priority;
  2. Protect your idea from being stolen with this time-stamped preprint article;
  3. Enhance the exposure and impact of your research;
  4. Receive feedback from your peers in advance;
  5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (3 papers)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
16 pages, 7776 KiB  
Article
Effect of Rockfall Spatial Representation on the Accuracy and Reliability of Susceptibility Models (The Case of the Haouz Dorsale Calcaire, Morocco)
Land 2024, 13(2), 176; https://doi.org/10.3390/land13020176 - 02 Feb 2024
Viewed by 498
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
Show Figures

Figure 1

22 pages, 10665 KiB  
Article
Design and Implementation of a Prototype Seismogeodetic System for Tectonic Monitoring
Sensors 2023, 23(21), 8986; https://doi.org/10.3390/s23218986 - 05 Nov 2023
Viewed by 802
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
Show Figures

Figure 1

37 pages, 24846 KiB  
Article
Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation
Land 2023, 12(5), 1018; https://doi.org/10.3390/land12051018 - 05 May 2023
Cited by 11 | Viewed by 2413
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
Show Figures

Figure 1

Back to TopTop