Modeling, Assessment and Monitoring of Geotechnical and Geological Disasters

Topic Information

Dear Colleagues,

Due to climate change and increasing human disturbances, the frequency and intensity of geotechnical and geological disasters have been rising, posing significant threats to lives, property, and critical infrastructure. This Topic explores the key challenges and emerging developments in this field, with a focus on the modeling, assessment, and monitoring of geotechnical and geological hazards, as well as the remote sensing interpretation and monitoring of geological hazards. It covers evolutionary mechanisms and process modeling, urban and mining disaster characterization and prevention, intelligent monitoring and early warning, and risk assessment and mitigation strategies, integrating earth sciences, engineering, remote sensing, and geographic information systems. This theme contributes to Goal 11 of the 2030 Agenda for Sustainable Development: Building inclusive, safe, disaster resilient, and sustainable cities and human settlements.

The theme “Modeling, Assessment and Monitoring of Geotechnical and Geological Disasters” welcomes both comprehensive review articles and high-quality original research. We invite contributions across relevant subtopics to build an interdisciplinary and diverse collection. We look forward to your submissions.

Dr. Ran Wang
Dr. Yun Wu
Dr. Jiakun Teng
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
GeoHazards geohazards	2.3	2.6	2020	18.9 Days	CHF 1400	Submit
Geosciences geosciences	2.3	4.4	2011	22.7 Days	CHF 1800	Submit
Land land	3.5	6.4	2012	16.4 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit
Safety safety	2.4	3.9	2015	34.4 Days	CHF 1800	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit

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Published Papers (6 papers)

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All GeoHazards Geosciences Land Safety Remote Sensing Applied Sciences Sensors Sustainability

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21 pages, 34876 KB  

Open AccessArticle

Background Characteristics and Seismic Response of M2 Tidal Parameters in Confined Aquifers of the Beijing Seismic Monitoring Well Network

by Yuxuan Chen, Fuqiong Huang, Leyin Hu, Zhiguo Wang, Kongyan Han, Peixue Hua, Xiaoru Sun, Mingbo Yang and Shijun Zhong

GeoHazards 2026, 7(4), 118; https://doi.org/10.3390/geohazards7040118 - 3 Oct 2026

Abstract

Groundwater level responses to solid tides in confined wells provide a means for investigating aquifer parameter variations and earthquake-related effects. Using hourly water-level observations from eight confined wells of the Beijing seismic monitoring well network (January 2017–December 2025), this study applied the Baytap-G [...] Read more.

Groundwater level responses to solid tides in confined wells provide a means for investigating aquifer parameter variations and earthquake-related effects. Using hourly water-level observations from eight confined wells of the Beijing seismic monitoring well network (January 2017–December 2025), this study applied the Baytap-G program to extract the M2 tidal constituent contribution, amplitude, tidal factor, and phase lag, and analyzed their temporal variations and responses to earthquakes. The eight wells are classified into five groups: Group 1 (BQ, CP), stable during non-interference intervals but with permanent instrument-induced jumps; Group 2 (MF, XJ), stable background references; Group 3 (LX), reflecting genuine hydrological processes; Group 4 (DHC, XXZ), signals too weak for reliable analysis; and Group 5 (WLY), dominated by persistent instrumental disturbances. Using mean ±3σ thresholds derived from seismically quiet periods, nine persistent ≥3σ anomaly segments were identified; none coincides in time with the 14 Ms ≥ 4.0 earthquakes or the 41 coseismic water-level response events, and all can be attributed to documented instrument events. The hypothesis that earthquakes alter M2 parameters before or after their occurrence is not supported, nor is the hypothesis that coseismic water-level responses produce short-term M2 parameter changes; the hypothesis that instrument operational conditions and long-term hydrological changes dominate the observed variations is supported. The absence of earthquake-triggered M2 parameter responses is attributed to basin attenuation, limited strain conversion efficiency, response mechanism decoupling, and observational resolution. These results provide a background reference for applying well water-level tidal parameters to earthquake precursor monitoring in the Beijing area, showing that independent baselines must be established for each well and that instrument operational records are necessary for anomaly identification. Full article

(This article belongs to the Topic Modeling, Assessment and Monitoring of Geotechnical and Geological Disasters)

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32 pages, 89911 KB  

Open AccessArticle

Homogeneous Terrain Unit Extraction by Integrating Superpixel Segmentation and Multiscale Region Merging: A Case Study in the Deeply Incised Valleys of Southeastern Tibet

by Zhongkang Yang, Shishu Zhang, Jianhui Deng, Jingen Ma, Qingchun Li, Jinbing Wei and Siyuan Zhao

Remote Sens. 2026, 18(17), 3028; https://doi.org/10.3390/rs18173028 - 4 Sep 2026

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Abstract

Mapping mountain surfaces requires spatial units that represent both hillslope-scale structure and local within-slope terrain heterogeneity. Hydrological slope units provide limited representation of within-slope objects, whereas general object-based segmentation is sensitive to fragmentation and scale selection. We developed a homogeneous terrain unit extraction [...] Read more.

Mapping mountain surfaces requires spatial units that represent both hillslope-scale structure and local within-slope terrain heterogeneity. Hydrological slope units provide limited representation of within-slope objects, whereas general object-based segmentation is sensitive to fragmentation and scale selection. We developed a homogeneous terrain unit extraction framework based on superpixel segmentation and multiscale region merging (SSM-HTU), in which initial slope units serve as local statistical references and within-slope terrain objects are generated through slope-unit-conditioned morphometric representation, superpixel initialization, distribution-sensitive region merging, and a nested partition hierarchy. The framework was applied to the 5136 km² Yuqu River Basin in southeastern Tibet. Of 971 expert-interpreted reference HTUs, 680 were reserved for independent geometric evaluation; 1329 historical landslides were additionally used for supplementary spatial association analysis across mapping-unit schemes. Relative to the eCognition Multiresolution Segmentation (MSS) baseline, SSM-HTU showed a slight decrease in Precision from 0.8432 to 0.8340, while Recall (directional reference-object coverage) increased from 0.7615 to 0.8011, area-weighted IoU from 0.6710 to 0.6945, and Boundary F1 at a 12.5 m tolerance from 0.5980 to 0.6810, indicating greater reference-object coverage, spatial overlap, and boundary correspondence without uniform improvement across all geometric metrics. Across four geomorphological zones, area-weighted IoU ranged from 0.671 to 0.724 and Boundary F1 from 0.651 to 0.709, with non-monotonic regional variation. Mapping-unit schemes also yielded factor-dependent spatially stratified associations, underscoring the importance of spatial support in downstream statistical analysis. SSM-HTU therefore provides an object-based mapping framework for representing local within-slope terrain heterogeneity within a hillslope-scale statistical context in deeply incised valleys. Full article

(This article belongs to the Topic Modeling, Assessment and Monitoring of Geotechnical and Geological Disasters)

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15 pages, 18661 KB  

Open AccessArticle

ChangeFormer-Based Detection of Landslide-Damaged Areas Using Sentinel-2 Imagery in South Korea

by Geonhwi Jung, Mooyoung Lim, Choongshik Woo, Bomi Kim, Yongku Kim, Yongchul Shin and Joowon Park

Land 2026, 15(9), 1601; https://doi.org/10.3390/land15091601 - 30 Aug 2026

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Abstract

Landslides triggered by heavy rainfall have become increasingly frequent and severe, creating a need for the rapid and accurate detection of damaged areas for post-disaster response and recovery planning. This study developed a ChangeFormer-based landslide damage detection model using single-channel differenced Normalized Difference [...] Read more.

Landslides triggered by heavy rainfall have become increasingly frequent and severe, creating a need for the rapid and accurate detection of damaged areas for post-disaster response and recovery planning. This study developed a ChangeFormer-based landslide damage detection model using single-channel differenced Normalized Difference Vegetation Index (dNDVI) imagery derived from pre- and post-event Sentinel-2 data. Landslide reference data were used to construct a patch-based training dataset, and model generalization was evaluated in Sancheong-gun and Hapcheon-gun, Gyeongsangnam-do, Republic of Korea, where landslide damage was reported following heavy rainfall in 2025. As available reference data differed between these regions, region-specific validation strategies were applied. In Sancheong-gun, polygon and point reference data were used for quantitative validation. All 12 reference-defined damaged sites were intersected by the model predictions, corresponding to a site-level detection rate of 100%. Point-based assessment showed an increasing distance-based detection rate with increasing positional tolerance, reaching 87.9% within the 80–100 m tolerance range. This result was interpreted as positional agreement between the reference points and predicted damaged areas rather than as overall accuracy. In Hapcheon-gun, where official polygon- and point-based reference data were unavailable, qualitative external validation using drone imagery indicated that the predicted areas were generally consistent with locations interpreted as landslide damage. These results suggest that the proposed framework is effective for the post-disaster spatial assessment of landslide-damaged areas. Full article

(This article belongs to the Topic Modeling, Assessment and Monitoring of Geotechnical and Geological Disasters)

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31 pages, 13967 KB  

Open AccessArticle

Adaptive Block Relative Edge Density (ABRED): An Interpretable Feature for Tunnel Surrounding Rock Fracture Identification

by Bolin Jiang, Yunxue Luo and Shanshan Wu

Sensors 2026, 26(17), 5488; https://doi.org/10.3390/s26175488 - 29 Aug 2026

Viewed by 357

Abstract

Fracture density estimation from tunnel face imagery is a critical step in rock mass classification and quantification of fragmentation severity. However, traditional texture descriptors have three inherent drawbacks: fixed spatial partitioning, sensitivity to illumination, and lack of modeling of local contrast variations. To [...] Read more.

Fracture density estimation from tunnel face imagery is a critical step in rock mass classification and quantification of fragmentation severity. However, traditional texture descriptors have three inherent drawbacks: fixed spatial partitioning, sensitivity to illumination, and lack of modeling of local contrast variations. To overcome these problems, we propose the Adaptive Block Relative Edge Density (ABRED) feature as a physically motivated, interpretable design for geological image interpretation. A quadtree decomposition guided by local gradient variance dynamically adjusts region granulation to be finer in areas with denser fracture and coarser otherwise. ABRED directly calculates relative edge density, where relative edge density is defined as the ratio of a block’s normalized edge count to the mean edge count of its eight neighboring blocks, demonstrating empirical robustness to illumination variations in our tests. Multi-scale representation is performed by aggregating Gaussian blur edge density maps on gradually blurring scales. Compared to fixed-grid competitors and handcrafted features, including LBP, HOG, and Gabor, ABRED achieves an overall classification performance of 87.6% on an operational, real-world dataset of 1200 tunnel surrounding rock images annotated with three unique fracture severity classes, significantly outperforming baseline fixed-grid methods and handcrafted feature approaches. Ablation studies confirm each component contributes individually, while PCA-based embedding results in well-separated, class-discriminative clusters. Importantly, by incorporating domain-specific physical information directly into its feature engineering—the opposite of data-driven black box optimization—ABRED achieves performance comparable to recent deep learning approaches on our specific dataset (87.6% vs. 88–91% reported for CNN-based methods under similar conditions), while requiring substantially lower computational overhead and offering full interpretability. We note, however, that deep learning models may achieve superior accuracy given substantially larger training datasets. The advantage of ABRED lies in its efficiency, transparency, and suitability for CPU-based field deployment where resources are constrained. Full article

(This article belongs to the Topic Modeling, Assessment and Monitoring of Geotechnical and Geological Disasters)

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21 pages, 1593 KB  

Open AccessArticle

A Secure Multi-Layer Edge-Based Sensor Architecture for Building-Level Disaster Monitoring and Decision Support

by Kerem Erzurumlu and Kenan Rıfat Erzurumlu

Sensors 2026, 26(14), 4531; https://doi.org/10.3390/s26144531 - 17 Jul 2026

Viewed by 578

Abstract

Natural and human-induced disasters can cause significant loss of life and property, particularly at the building level, highlighting the need for effective early detection, real-time monitoring, and rapid post-disaster response. Current disaster management approaches largely rely on citizen reports and manual observations, which [...] Read more.

Natural and human-induced disasters can cause significant loss of life and property, particularly at the building level, highlighting the need for effective early detection, real-time monitoring, and rapid post-disaster response. Current disaster management approaches largely rely on citizen reports and manual observations, which may lead to delays and inefficient resource allocation, especially in large-scale events. This study proposes a secure, modular, multi-layer disaster monitoring and decision-support architecture that integrates sensor-based building-edge monitoring units deployed at both the building and apartment levels with a central emergency monitoring system. The architecture comprises three main layers: edge sensing, secure cellular communication, and central decision-making. Building-edge monitoring units collect data related to structural motion and inclination indicators, fire, flooding, and gas leaks, perform preliminary processing, and transmit aggregated data securely to the central system. Communication security is ensured through a certificate-based authentication mechanism supported by a dedicated certificate authority, reducing the risk of unauthorized access and fraudulent data injection. The central system performs automated event detection and separately evaluates physical building condition and communication status, enabling prioritized response planning. To evaluate feasibility, a two-building prototype was implemented and tested through scenario-based experiments involving two independently operating building-edge monitoring units connected to the same central monitoring system. The prototype demonstrated concurrent secure data acquisition and central aggregation from two buildings; however, district- and regional-scale performance requires further validation through larger-scale controlled load tests and field deployments. Under laboratory conditions, the prototype demonstrated sensor-data acquisition, authenticated transmission, and centralized event classification. End-to-end latency and building-edge monitoring unit power consumption were also measured; however, the prototype was not validated under environmental conditions representative of real disasters. Overall, the findings suggest that sensor-based, secure, and centralized monitoring systems may complement traditional disaster management approaches. Full article

(This article belongs to the Topic Modeling, Assessment and Monitoring of Geotechnical and Geological Disasters)

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22 pages, 18001 KB  

Open AccessArticle

Geological Hazard Assessment in the Yili River Valley Based on the Coupled Model of WOE-BPNN-SHAP

by Jiming Ma, Yong Tian and Yanjuan Tang

Sustainability 2026, 18(14), 6939; https://doi.org/10.3390/su18146939 - 8 Jul 2026

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Abstract

The Yili River Valley in Xinjiang is characterized by complex geological structures and frequent geological hazards, which seriously threaten local lives, property, and infrastructure. Improving the accuracy and interpretability of geological hazard assessment is therefore of great significance. To address this, nine factors, [...] Read more.

The Yili River Valley in Xinjiang is characterized by complex geological structures and frequent geological hazards, which seriously threaten local lives, property, and infrastructure. Improving the accuracy and interpretability of geological hazard assessment is therefore of great significance. To address this, nine factors, including elevation, distance from fault, and slope, were selected to construct a WOE-BPNN-SHAP coupled model. The weights of evidence (WOE) method was first used for factor correlation testing and to optimize the input of the BP neural network. The evaluation accuracies of WOE, WOE-DNN, and WOE-BP models were then compared, and the SHAP model was introduced to analyze the coupling relationships among factors. Results show that the WOE-BP model achieves the best predictive performance, with an AUC of 83.65%. Areas of extremely high-risk account for 8.63% of the study area, while higher-risk areas account for 15.39%. Elevation (1688–2847 m), distance from fault (<3000 m), precipitation (192.6–290.8 mm), and slope (>16°) are identified as the main driving factors. This coupled method provides a new technical approach for regional geological hazard assessment and offers a theoretical basis for disaster prevention, mitigation, and resilience building in the Yili River Valley. Full article

(This article belongs to the Topic Modeling, Assessment and Monitoring of Geotechnical and Geological Disasters)

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