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GeoHazards
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Modern Technologies in Understanding, Monitoring and Preventing Geohazards and Associated...
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Modern Technologies in Understanding, Monitoring and Preventing Geohazards and Associated Risks and Disasters

A Special Issue of GeoHazards (ISSN 2624-795X).

Deadline for manuscript submissions: 30 November 2026 | Viewed by 6527

Editors

Dr. Gerassimos A. Papadopoulos

E-Mail Website
Guest Editor
Hellenic Mediterranean University, 71410 Heraklion, Greece
Interests: natural hazards research; civil protection
Special Issues, Collections and Topics in MDPI journals
Dr. Stavros Kalogiannidis

E-Mail Website
Guest Editor
Department of Business Administration, Faculty of Economic Sciences, University of Western Macedonia, 51100 Grevena, Greece
Interests: crisis management; civil protection and operational management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Billions of people across our planet are threatened by endogenous and exogenous geohazards originating from varied geological, meteorological and climate change processes, as well as human activity. The role of modern technologies is vital in understanding the mechanisms driving geohazards and monitoring their evolution, as well as in preventing the potential risks and disasters. This Special Issue covers the wide spectrum of theoretical, applied and operational approaches that are related to modern technologies and are available or under development for protecting societies from geohazards. This Special Issue will bring together contributions from scientists, technology developers and practitioners working on the above-mentioned hot topics.

Dr. Gerassimos A. Papadopoulos
Dr. Stavros Kalogiannidis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. GeoHazards is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • geohazards
  • understanding mechanisms
  • monitoring
  • prevention
  • modern technologies
  • risk mitigation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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28 pages, 11414 KB  
Article
Monitoring and Prediction of Subsidence in Mining Areas of Liaoyuan Northern New District Based on InSAR Technology
by Menghao Li, Yichen Zhang, Jiquan Zhang, Zhou Wen, Jintao Huang and Haoying Li
GeoHazards 2026, 7(1), 17; https://doi.org/10.3390/geohazards7010017 - 1 Feb 2026
Cited by 3 | Viewed by 1817
Abstract
Ground subsidence in mined-out areas has irreversible impacts on residents’ lives and infrastructure, making its monitoring and prediction crucial for ensuring safety, protecting the ecological environment, and promoting sustainable development. This study employed the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique [...] Read more.
Ground subsidence in mined-out areas has irreversible impacts on residents’ lives and infrastructure, making its monitoring and prediction crucial for ensuring safety, protecting the ecological environment, and promoting sustainable development. This study employed the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to process Sentinel-1A satellite images of Liaoyuan’s Northern New District from August 2022 to March 2025, deriving ground deformation data. The SBAS-InSAR results were validated using unmanned aerial vehicle (UAV) measurements. Monitoring revealed deformation rates ranging from −26.80 mm/year (subsidence) to 13.12 mm/year (uplift) in the area, with a maximum cumulative subsidence of 59.59 mm observed near the Xi’an Sixth District. Based on spatiotemporal patterns, most mining-induced subsidence in the study area is in its late stage, primarily caused by progressive compaction of fractured rock masses and voids within the collapse and fracture zones. Using subsidence data from August 2022 to March 2024, three prediction models—LSTM, GRU, and TCN-GRU—were trained and subsequently applied to forecast subsidence from March 2024 to August 2025. Comparisons between the predictions and SBAS-InSAR measurements showed that all models achieved high accuracy. Among them, the TCN-GRU model yielded predictions closest to the actual values, with a correlation coefficient exceeding 0.95, validating its potential for application in time-series settlement monitoring. Full article
(This article belongs to the Special Issue Modern Technologies in Understanding, Monitoring and Preventing Geohazards and Associated Risks and Disasters)
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Review

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11 pages, 581 KB  
Review
Lake Sarez and the Usoi Dam in Tajikistan: Hazard Assessment, Stability and Risk Management Perspectives
by Zafarjon Sultonov and Hari K. Pant
GeoHazards 2026, 7(3), 80; https://doi.org/10.3390/geohazards7030080 - 1 Jul 2026
Viewed by 1014
Abstract
Lake Sarez in Tajikistan, formed by a major earthquake-induced landslide in 1911, is located in the highly seismically active Pamir–Hindu Kush region. The lake is impounded by the Usoi Dam, one of the largest natural landslide dams in the world, which has raised [...] Read more.
Lake Sarez in Tajikistan, formed by a major earthquake-induced landslide in 1911, is located in the highly seismically active Pamir–Hindu Kush region. The lake is impounded by the Usoi Dam, one of the largest natural landslide dams in the world, which has raised concerns regarding its long-term stability and associated downstream flood hazards. Due to its geomorphological setting and potential exposure to multiple triggering mechanisms, including seismic activity and landslides, Lake Sarez is widely considered a high-consequence hazard system. Although the dam has remained stable for over a century and is currently monitored using modern geodetic and satellite-based technologies, uncertainties remain regarding its internal structure and response to extreme external forcing. While existing early warning systems enhance preparedness in downstream communities, effective long-term risk reduction requires continued monitoring, improved hazard modeling, and strengthened regional cooperation. This review synthesizes existing studies on the geological setting, hazard potential, stability assessments, and disaster risk management strategies related to Lake Sarez. It highlights the importance of integrated multi-hazard analysis and precautionary risk governance in managing low-probability but high-impact natural dam failure scenarios. Full article
(This article belongs to the Special Issue Modern Technologies in Understanding, Monitoring and Preventing Geohazards and Associated Risks and Disasters)
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21 pages, 1871 KB  
Review
A Critical Review of Wildfire Risk Prediction Models in Data-Scarce Mediterranean Environments
by Hajar Mrabet, Ibtissam Latachi, Tajjeeddine Rachidi and Mohammed Karim
GeoHazards 2026, 7(2), 76; https://doi.org/10.3390/geohazards7020076 - 16 Jun 2026
Viewed by 783
Abstract
Wildfires are a growing threat in Mediterranean regions where climate variability and land-use practices increase vulnerability to fire risk. Developing effective prediction models is essential for robust wildfire management, particularly in such data-scarce environments. Focusing on data-scarce Mediterranean environments, with reference to environmental [...] Read more.
Wildfires are a growing threat in Mediterranean regions where climate variability and land-use practices increase vulnerability to fire risk. Developing effective prediction models is essential for robust wildfire management, particularly in such data-scarce environments. Focusing on data-scarce Mediterranean environments, with reference to environmental conditions observed in Morocco, this review presents prediction models across three methodological categories: spatial risk mapping, temporal forecasting, and fire spread simulation, alongside the satellite data products that support their deployment. Each category is assessed in terms of predictive performance, data requirements, and adaptability to low-resource environments. XGBoost showed strong applicability in data-scarce Mediterranean contexts, while ARIMA was validated for forecasting fire-relevant time series under limited data resources. Freely accessible MODIS-derived products represent a significant asset to the region. Based on this synthesis, a hybrid XGBoost-ARIMA framework incorporating MODIS-derived inputs and SHAP-based interpretability is proposed as a promising candidate architecture to be validated after further investigation. The findings aim to support researchers, land managers, and policymakers in strengthening local wildfire prevention and mitigation efforts by aligning model capabilities with regional data and environmental constraints. Full article
(This article belongs to the Special Issue Modern Technologies in Understanding, Monitoring and Preventing Geohazards and Associated Risks and Disasters)
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17 pages, 2086 KB  
Review
Research Progress on Intelligent Fault Recognition Technology in Seismic Exploration
by Ke Ren, Cheng Song, Na Li, Xiaodong Wang, Zeming Wang and Yanhai Liu
GeoHazards 2026, 7(2), 48; https://doi.org/10.3390/geohazards7020048 - 29 Apr 2026
Viewed by 849
Abstract
With the expansion of seismic exploration targets to deeper and more complex geological structures, traditional fault interpretation methods face significant challenges in terms of efficiency and accuracy. The extensive application of artificial intelligence (AI) technologies is driving the evolution of fault recognition techniques [...] Read more.
With the expansion of seismic exploration targets to deeper and more complex geological structures, traditional fault interpretation methods face significant challenges in terms of efficiency and accuracy. The extensive application of artificial intelligence (AI) technologies is driving the evolution of fault recognition techniques toward automation and intelligence. This paper systematically reviews the development of AI technologies in fault recognition, from traditional machine learning-based seismic attribute fusion analysis to deep learning-based end-to-end recognition and semantic segmentation. It provides a detailed discussion of key technological advancements, such as sample set construction, weak signal enhancement, and noise suppression. To address the current challenges, including the insufficient authenticity of synthetic data, poor model interpretability, and weak quantitative representation capabilities, this study proposes three future research directions: the development of benchmark datasets based on real geological evolution, the construction of interpretable model architectures that incorporate geological prior information, and the realization of multi-parameter collaborative intelligent fault system analysis. These directions aim to provide theoretical support for advancing the practical and industrial applications of intelligent fault recognition technology. Full article
(This article belongs to the Special Issue Modern Technologies in Understanding, Monitoring and Preventing Geohazards and Associated Risks and Disasters)
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