Zhuo Chen1,2*
Davide Tiranti3
Omid Ghorbanzadeh4
Danqing Song5,6
Mukhiddin Juliev7,8
E. Bruce Pitman9
Thomas Oommen10- 1College of Civil Engineering, Sichuan Agricultural University, Dujiangyan, Sichuan, China
- 2Sichuan Higher Education Engineering Research Center for Disaster Prevention and Mitigation of Village Construction, Sichuan Agricultural University, Dujiangyan, China
- 3Regional Agency for Environmental Protection of Piemonte (Arpa Piemonte), Turin, Italy
- 4University of Natural Resources and Life Sciences, Institute of Geomatics Peter-Jordan-Straße 82, Vienna, Austria
- 5School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong, China
- 6State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, Guangdong, China
- 7Institute of Fundamental and Applied Research, “TIIAME” National Research University, Tashkent, Uzbekistan
- 8Turin Polytechnic University in Tashkent, Tashkent, Uzbekistan
- 9Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY, United States
- 10Department of Geology and Geological Engineering, University of Mississippi, University, MS, United States
Editorial on the Research Topic
Advances and applications in modeling, assessment, and mitigation of landslide disasters
Introduction
In recent decades, natural hazards and anthropogenic-induced disasters such as earthquakes, landslides, rockfalls, debris flows, rainstorms, floods, tunnel collapses, dam failures, and forest fires have posed major challenges. These events necessitate efforts to mitigate risks and safeguard structures, infrastructure, economic activities, and human lives, especially in mountainous areas (Cutter et al., 2015; Zou et al., 2018; Klein et al., 2019; Adler et al., 2020; Alcántara-Ayala and Geertsema, 2022).
Landslides: a persistent threat
Landslides are among the most common and destructive natural disasters worldwide, threatening human lives, properties, and infrastructure safety (Chen and Song, 2023; Chen D. et al., 2024; Liu et al., 2024; Zhang Q. et al., 2024; CRED, 2023; Kennedy et al., 2015; Froude and Petley, 2018; Fidan et al., 2024). They are characterized by intricate formation mechanisms, nonlinear deformation, and uncertainty, necessitating multidisciplinary approaches for their analysis and prediction.
Advances in monitoring and prediction
Scientific research has focused on enhancing understanding and developing technologies for effective risk mitigation against landslides (Carrión-Mero et al., 2021). Advanced monitoring tools like InSAR (Interferometric Synthetic Aperture Radar) (Scaioni et al., 2014; Xu et al., 2023), UAV (Unmanned Aerial Vehicles) (Sun et al., 2024; Zhang Z. et al., 2024), Fiber Optics (Ivanov et al., 2021; Brezzi et al., 2023), Beidou (Huang et al., 2023), and MEMS (Micro-Electro-Mechanical System) have been employed (Barzegar et al., 2023).
Yang et al. used multi-temporal InSAR techniques combined with geospatial statistical analysis to study the Muyuba landslide in China. Findings revealed continuous subsidence largely influenced by drainage networks, rock strata orientation, and reservoir water level variations, linking anthropogenic activities with increased landslide risk.
Wang et al. applied wavelet transform and ARIMA models for landslide displacement prediction. The ARIMA model demonstrated high accuracy, with a root mean square error (RMSE) of 4.52 mm, confirming its effectiveness in specific conditions. This model’s applicability was further validated in practical scenarios.
Innovative modeling techniques
Numerical methods such as CDEM continuum-based discrete element method) (Li et al., 2024), 3D-DDA (three-dimensional discontinuous deformation analysis) (Wang et al., 2021; Ma and Liu, 2022), NMM (numerical manifold method) (Cao et al., 2024; Zhang and Li, 2024), SPH (smoothed particle hydrodynamics) (Dai et al., 2014; Mahallem et al., 2022; Chen W. et al., 2024), MPM (material point method) (Conte et al., 2019; Yamaguchi et al., 2023), and LBM (Lattice Boltzmann Method) (Krüger et al., 2016), provide deeper insights into landslide dynamics. Research contributions highlight the integration of machine learning and artificial intelligence to predict soil evaporation rates using models like KNORA (Kainthura and Sharma, 2022; Tehrani et al., 2022; Youssef et al., 2023; Zhang and Wang, 2024).
Priyanka et al. utilized machine learning to predict soil evaporation, employing a novel feature selection technique to enhance accuracy. Their findings underscore the superiority of certain ML models in predicting environmental phenomena.
Testing and laboratory advances
Laboratory techniques have been refined to better understand influential factors in gravitational phenomena (Hemeda, 2018; Menon and Kolathayar, 2024). Guo et al. focused on soil stability through wet and dry compaction tests, shedding light on risks associated with field compaction variations.
Zhao et al. analyzed water inrush disasters in coal seam mining through numerical simulations. Findings emphasized the role of mining stress and confined water in crack propagation along hidden faults, offering insights for preventing water inrush incidents.
Mitigation and defense structure design
Effective defense structure design significantly reduces landslide impact on infrastructure (Luo et al., 2023). Barbini et al. proposed methods to control sediment volume in debris flows through deposition areas and retention basins, confirmed through hydraulic modeling.
Future directions and studies
Research on landslide dynamics continues to evolve, with interdisciplinary efforts advancing our understanding of geological disaster science. This ongoing pursuit provides a scientific foundation for forecasting and mitigating geological hazards.
We express gratitude to all contributors to this Research Topic, advancing the modeling, assessment, and mitigation of landslide disasters. This compilation serves as a valuable resource, inspiring further studies in this critical field.
Author contributions
ZC: Writing–original draft, Writing–review and editing. DT: Writing–review and editing, Writing–original draft. OG: Writing–review and editing. DS: Writing–review and editing. MJ: Writing–review and editing. EP: Writing–review and editing. TO: Writing–review and editing.
Funding
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was funded by the National Natural Science Foundation of China (52208359), the Natural Science Foundation of Sichuan Province (2024NSFSC0925), the National Natural Science Foundation of China (52109125), and the Fundamental Research Funds for the Central Universities (2023ZYGXZRx2tjD2231010).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Keywords: landslides, laboratory experiments, computational geosciences, numerical simulation, stability analysis, spatial distribution, monitoring method, disaster mitigation
Citation: Chen Z, Tiranti D, Ghorbanzadeh O, Song D, Juliev M, Pitman EB and Oommen T (2024) Editorial: Advances and applications in modeling, assessment, and mitigation of landslide disasters. Front. Earth Sci. 12:1480635. doi: 10.3389/feart.2024.1480635
Received: 14 August 2024; Accepted: 27 November 2024;
Published: 06 December 2024.
Edited and reviewed by:
Gordon Woo, Risk Management Solutions, United KingdomCopyright © 2024 Chen, Tiranti, Ghorbanzadeh, Song, Juliev, Pitman and Oommen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zhuo Chen, MTM4ODI1MzUwMDlAMTYzLmNvbQ==