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 (; ; ; ; ).
Landslides: a persistent threat
Landslides are among the most common and destructive natural disasters worldwide, threatening human lives, properties, and infrastructure safety (; ; ; ; ; ; ; ). 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 (). Advanced monitoring tools like InSAR (Interferometric Synthetic Aperture Radar) (; ), UAV (Unmanned Aerial Vehicles) (; ), Fiber Optics (; ), Beidou (), and MEMS (Micro-Electro-Mechanical System) have been employed ().
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) (), 3D-DDA (three-dimensional discontinuous deformation analysis) (; Ma and Liu, 2022), NMM (numerical manifold method) (; ), SPH (smoothed particle hydrodynamics) (; Mahallem et al., 2022; ), MPM (material point method) (; ), and LBM (Lattice Boltzmann Method) (), 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 (; ; ; ).
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 (; ). 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 (). 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.
Statements
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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Summary
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
Volume
12 - 2024
Edited and reviewed by
Gordon Woo, Risk Management Solutions, United Kingdom
Updates
Copyright
© 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, 13882535009@163.com
Disclaimer
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.