GeoHazards

12 pages, 3244 KB

Open AccessArticle

Landslide Susceptibility Mapping in the Mount Elgon Districts of Eastern Uganda Using Google Earth Engine

by Mohammed Mussa Abdulahi, Pascal E. Egli and Zinabu Bora

GeoHazards 2026, 7(2), 50; https://doi.org/10.3390/geohazards7020050 - 30 Apr 2026

Landslides are a critical environmental hazard in mountainous regions like eastern Uganda, posing serious threats to lives, infrastructure, and ecosystems. While recent advances in geospatial technology have improved hazard assessment, existing research often lacks high-resolution, cloud-based analysis for dynamic landscapes such as the [...] Read more.

Landslides are a critical environmental hazard in mountainous regions like eastern Uganda, posing serious threats to lives, infrastructure, and ecosystems. While recent advances in geospatial technology have improved hazard assessment, existing research often lacks high-resolution, cloud-based analysis for dynamic landscapes such as the Mount Elgon region. This study addresses that gap by developing a landslide susceptibility map (LSM) using Google Earth Engine (GEE), which integrates remote sensing and geospatial data for scalable analysis. The main objective is to identify landslide-prone zones by analyzing eight conditioning factors, namely slope, elevation, vegetation cover, rainfall, land use land cover, soil type, soil moisture, and groundwater levels using the weighted overlay method (WOM). The methodology produced a classified LSM with zones of high (37.7%), moderate (58%), low (2%), and very low (2.3%) susceptibility, with validation via historical landslide data and ROC analysis yielding an AUC of 0.76, confirming strong predictive performance. The study underscores the value of GEE in hazard modeling and provides actionable insights for targeted risk mitigation, sustainable land use planning, and early warning system development in landslide-prone areas. Full article

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19 pages, 54120 KB

Open AccessArticle

The Gassy Sediments of the Cilento Offshore (Southern Tyrrhenian Sea, Italy) and Their Impact on the Marine Hazard Offshore the Cilento Promontory

by Gemma Aiello

GeoHazards 2026, 7(2), 49; https://doi.org/10.3390/geohazards7020049 - 30 Apr 2026

Abstract

In order to assess their influence on the marine hazard offshore the Cilento Promontory, the gassy sediments of the Cilento offshore have been thoroughly examined using the geological interpretation of a closely spaced grid of Sub-bottom Chirp profiles. Based on the general stratigraphic [...] Read more.

In order to assess their influence on the marine hazard offshore the Cilento Promontory, the gassy sediments of the Cilento offshore have been thoroughly examined using the geological interpretation of a closely spaced grid of Sub-bottom Chirp profiles. Based on the general stratigraphic framework three areas have been previously identified, highlighting the different acoustic features occurring in the Cilento area. The acoustic anomalies include acoustic blanking, shallow gas pockets, and seismic units impregnated of gas, showing distinct acoustic responses. Understanding these anomalies and the related seismo-stratigraphic units in the offshore Cilento Promontory provides a valuable foundation for evaluating marine geohazards and may assist in developing strategies to mitigate geohazards in the Cilento area. Full article

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17 pages, 2086 KB

Open AccessReview

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

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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23 pages, 5852 KB

Open AccessArticle

Probabilistic Seismic Hazard Assessment of Armenia Using an Integrated Seismotectonic Framework

by Mikayel Gevorgyan, Arkadi Karakhanyan, Avetis Arakelyan, Suren Arakelyan, Hektor Babayan, Gevorg Babayan, Elya Sahakyan and Lilit Sargsyan

GeoHazards 2026, 7(2), 47; https://doi.org/10.3390/geohazards7020047 - 28 Apr 2026

Abstract

Armenia is located within the central segment of the Arabia–Eurasia continental collision zone and is exposed to significant seismic hazard. This study presents an updated probabilistic seismic hazard assessment (PSHA) for Armenia based on an integrated seismotectonic framework incorporating active fault data, paleoseismological [...] Read more.

Armenia is located within the central segment of the Arabia–Eurasia continental collision zone and is exposed to significant seismic hazard. This study presents an updated probabilistic seismic hazard assessment (PSHA) for Armenia based on an integrated seismotectonic framework incorporating active fault data, paleoseismological evidence, and historical and instrumental seismicity. A hybrid seismic source model was developed by combining fault-based characteristic earthquake sources with distributed background seismicity. Hazard calculations were performed using the OpenQuake engine within a logic-tree framework to account for epistemic uncertainties in earthquake occurrence and ground-motion prediction. Ground motion was estimated using a weighted set of ground motion prediction equations (GMPEs). Peak ground acceleration (PGA) hazard maps were computed for several return periods, with emphasis on the 475-year return period (10% probability of exceedance in 50 years). The results indicate PGA values across Armenia ranging from approximately 0.2 g to 0.5 g, with the highest hazard levels in northwestern Armenia along the Pambak–Sevan–Syunik Fault System. Hazard deaggregation shows that seismic hazard in major Armenian cities is primarily controlled by shallow earthquakes with magnitudes Mw 6.8–7.4 occurring within ~30 km of urban centers. The results provide a scientific basis for seismic hazard assessment, zonation, and earthquake risk mitigation in Armenia. Full article

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17 pages, 8572 KB

Open AccessArticle

Experimental Study on Pressure Wave Propagation in Mine Ventilation Disasters

by Shouguo Yang, Shuxin Mei, Xiaofei Zhang and Jun Liang

GeoHazards 2026, 7(2), 46; https://doi.org/10.3390/geohazards7020046 - 28 Apr 2026

Abstract

This study experimentally investigates the propagation characteristics of static pressure waves (S-waves) and dynamic pressure waves (D-waves) induced by coal and gas outbursts of varying intensities, utilizing a self-built 1:30 scaled laboratory mine ventilation model. Systematic measurements and quantitative [...] Read more.

This study experimentally investigates the propagation characteristics of static pressure waves (S-waves) and dynamic pressure waves (D-waves) induced by coal and gas outbursts of varying intensities, utilizing a self-built 1:30 scaled laboratory mine ventilation model. Systematic measurements and quantitative analyses were conducted to determine waveform morphology, propagation velocities, attenuation laws, and frequency distributions. The results demonstrate that outburst-induced D-waves exhibit a distinct full-sinusoidal waveform, whereas S-waves present a half-sinusoidal profile. Notably, the wavelength of both wave types remains highly stable regardless of initial outburst intensity and propagation distance. Conversely, the wave amplitude is positively correlated with the outburst intensity and attenuates progressively with distance. Furthermore, D-waves demonstrate a significantly higher sensitivity to propagation distance than S-waves. Spectral analysis confirms that the primary energy of both pressure waves is concentrated in the ultra-low-frequency range below 1.0 Hz. The average propagation velocities of S-waves and D-waves were measured at 395.67 m/s and 280.27 m/s, respectively, indicating that S-waves propagate considerably faster. It should be noted that since these findings were derived under scaled laboratory conditions, direct extrapolation to full-scale, long-distance field roadways requires further validation. Ultimately, this work elucidates the fundamental propagation mechanisms of attenuated pressure waves within mine ventilation networks, providing critical waveform signatures for the remote identification and localization of underground disaster sources. Full article

(This article belongs to the Topic Dynamic Disaster Control, Mine Multi-Source Disaster Monitoring and Intelligent Analysis, 2nd Edition)

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31 pages, 12309 KB

Open AccessArticle

Spatial Analysis of Earthquake Risk in Şanlıurfa City Center

by Osman Nasanlı and Devrim Türkan Kejanlı

GeoHazards 2026, 7(2), 45; https://doi.org/10.3390/geohazards7020045 - 24 Apr 2026

Abstract

Population growth and unplanned land use significantly contribute to transforming natural hazards into disasters. Earthquake-induced losses of life and property are often linked to inadequate planning decisions. The city center of Şanlıurfa provides a recent example, where the 6 February 2023 earthquake resulted [...] Read more.

Population growth and unplanned land use significantly contribute to transforming natural hazards into disasters. Earthquake-induced losses of life and property are often linked to inadequate planning decisions. The city center of Şanlıurfa provides a recent example, where the 6 February 2023 earthquake resulted in 340 fatalities and substantial material damage. Variations in urban planning over different periods have caused disaster risk to fluctuate even across short distances. This study examines Şanlıurfa’s urban development in terms of earthquake vulnerability. Using Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP), the earthquake risk map reveals elevated risk in areas near fault lines and regions with high groundwater levels. Approximately 7% of the area is classified as very low risk, 54% as low risk, 37% as moderate risk, and 2% as high risk. Limited consideration of disaster-focused planning has led to both planned and unplanned developments in hazardous zones. Consequently, construction should prioritize low-risk areas, with necessary precautions applied in high-risk zones when unavoidable. Full article

19 pages, 20549 KB

Open AccessArticle

Analysis of Fault Slip Potential of Seismogenic Faults Based on In Situ Stress Measurement and Monitoring Data—A Case Study of the Strong Seismic Region in Zhangbei, North China

by Jing Meng, Yulu Fan, Chengjun Feng, Peng Zhang, Bangshen Qi and Chengxuan Tan

GeoHazards 2026, 7(2), 44; https://doi.org/10.3390/geohazards7020044 - 15 Apr 2026

Abstract

The aim of this paper is to investigate dynamic adjustment of the in situ stress field and the stability of main faults in the Zhangbei strong seismic region. Firstly, we utilized in situ stress measurement and monitoring data to discuss the dynamic adjustment [...] Read more.

The aim of this paper is to investigate dynamic adjustment of the in situ stress field and the stability of main faults in the Zhangbei strong seismic region. Firstly, we utilized in situ stress measurement and monitoring data to discuss the dynamic adjustment process of the in situ stress field. Subsequently, the Fault Slip Potential (FSP) v.1.0 software package was employed to calculate the fault slip potential of the main faults. Finally, the potential hazard of fault activity was assessed. The conclusions are as follows. (1) Since November 2015, the in situ stress field has been primarily influenced by NEE compressive tectonic action, with a slight enhancement in the near SN compressive tectonic action. (2) In the initial stage, NE-trending faults exhibited the highest stress accumulation levels, with near-EW-trending faults the lowest. Influenced by the enhanced near-SN-trending compressive action, as of 19 October 2020, near-EW-trending faults displayed the highest stress accumulation, followed by NW-trending faults, with NE-trending faults showing the least accumulation. (3) From November 2015 to October 2020, the in situ stress field was in a continuous accumulation process. Using the Shangyi–Pingquan fault as a boundary, fault activity in the southern part of the strong seismic region is more hazardous than that in the northern part. Full article

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16 pages, 6393 KB

Open AccessArticle

Spatiotemporal Variations in Population Exposure to Earthquake Disaster in Hubei Province Under Future SSP Scenarios

by Xiaoyi Hu, Jian Ye, Yani Huang, Haolin Liu, Menghao Zhai and Xue Li

GeoHazards 2026, 7(2), 43; https://doi.org/10.3390/geohazards7020043 - 14 Apr 2026

Abstract

This study develops a framework to capture spatiotemporal population dynamics and assess future earthquake exposure risk, using Hubei Province as a case study. Future population changes at the county level were projected under different shared socioeconomic pathways (SSPs). These projections were then integrated [...] Read more.

This study develops a framework to capture spatiotemporal population dynamics and assess future earthquake exposure risk, using Hubei Province as a case study. Future population changes at the county level were projected under different shared socioeconomic pathways (SSPs). These projections were then integrated with NPP-VIIRS nighttime light data and the normalized difference vegetation index (NDVI) to simulate the spatiotemporal dynamics of the population from 2020 to 2070 at a 500 m grid resolution. Combined with seismic hazard zoning, the evolution of population exposure risk under different pathways was assessed. The results indicate the following: 1. Different SSPs profoundly influence future population exposure patterns. Under the SSP3 (regional rivalry) pathway, population growth is the fastest with the strongest agglomeration effect and significantly elevated exposure levels. 2. The refined spatiotemporal population model can more realistically reveal the heterogeneity and evolutionary trajectory of population distribution, providing a high-precision data foundation for exposure analysis and effectively enhancing the scientific rigor of risk assessment. 3. Population exposure risk under various pathways exhibits distinct spatiotemporal dynamics, and monitoring its evolution under different scenarios helps identify high-risk counties that require priority attention. This study is expected to provide precise scientific evidence for implementing differentiated disaster prevention and mitigation strategies and territorial spatial resilience planning in Hubei Province, while it demonstrates the forward-looking value of combining long-term scenario simulations with refined exposure assessments. Full article

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16 pages, 3544 KB

Open AccessPerspective

Bridging Science and Governance for Earthquake Resilience in Malawi: A Perspective from the Southern East African Rift System

by Patsani Gregory Kumambala, Grivin Chipula, Ponyadira Corner and Chikondi Makwiza

GeoHazards 2026, 7(2), 42; https://doi.org/10.3390/geohazards7020042 - 13 Apr 2026

Abstract

Malawi lies within the southern segment of the East African Rift System and is exposed to infrequent but potentially damaging earthquakes. While recent advances in fault mapping, seismic monitoring, and hazard modelling have substantially improved scientific understanding of earthquake hazard in the Malawi [...] Read more.

Malawi lies within the southern segment of the East African Rift System and is exposed to infrequent but potentially damaging earthquakes. While recent advances in fault mapping, seismic monitoring, and hazard modelling have substantially improved scientific understanding of earthquake hazard in the Malawi Rift Zone, the practical reduction in seismic risk remains limited. This Perspective paper argues that earthquake resilience in Malawi is constrained less by scientific uncertainty than by challenges in integrating existing hazard knowledge into governance, planning, and preparedness. Drawing exclusively on published geological, geophysical, engineering, and policy literature, the paper synthesises evidence on seismic hazard, historical earthquake impacts, institutional preparedness, and barriers to the operational use of scientific risk assessments. An integrated, multi-pillar framework is proposed to support improved coordination between science, governance, infrastructure practice, and community preparedness. The framework is conceptual in nature and is intended to inform policy dialogue, prioritisation, and future empirical research rather than to provide a validated operational model. While grounded in the Malawian context, the insights presented are relevant to other low-income, rift-hosted regions facing similar challenges in translating earthquake science into effective disaster risk reduction. Full article

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15 pages, 6631 KB

Open AccessArticle

Evaluating the Deterministic Ground Shaking of Camarines Norte, the Philippines, Using the Rapid Earthquake Damage Assessment System and GIS

by Rhommel N. Grutas, Margarita P. Dizon, Gilbert A. Ramilo, Jeanne Benette P. Pabello and Maria Leonila P. Bautista

GeoHazards 2026, 7(2), 41; https://doi.org/10.3390/geohazards7020041 - 8 Apr 2026

Abstract

Prior studies have shown that socio-economic and structural risks can be correlated with earthquake effects. The quantification of these effects was used to formulate robust disaster risk reduction (DRR) strategies and building codes. This is more pronounced in countries with complex tectonic settings, [...] Read more.

Prior studies have shown that socio-economic and structural risks can be correlated with earthquake effects. The quantification of these effects was used to formulate robust disaster risk reduction (DRR) strategies and building codes. This is more pronounced in countries with complex tectonic settings, such as the Philippines, where strong-to-major earthquakes can occur. Here, we report the evaluation of deterministic ground shaking (GS) intensity measurements for Camarines Norte, the Philippines, with the objective of assessing and mapping the susceptibility of communities to intense ground motion. GS intensities and peak ground acceleration (PGA) were computed using the Rapid Earthquake Damage Assessment System (REDAS) software developed by the Philippine Institute of Volcanology and Seismology (PHIVOLCS). The PGA was computed as a fraction of acceleration due to gravity, while GS used the PHIVOLCS Earthquake Intensity Scale (PEIS). Simulations were based on recorded earthquakes and mapped active faults near the province. Geographic information systems were used to stack and refine each simulation. Results showed that 13 earthquakes and 13 seismic source zones classified most of the province as PEIS VIII or higher, with the PGA maximum at 0.66 g. The results implied that the province is susceptible to very destructive to completely devastating ground shaking, and it is recommended to incorporate these results into DRR policymaking. Full article

(This article belongs to the Collection Geohazard Characterization, Modeling, and Risk Assessment)

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27 pages, 2665 KB

Open AccessReview

Toward Knowledge-Enhanced Geohazard Intelligence: A Review of Knowledge Graphs and Large Language Models

by Wenjia Li and Yongzhang Zhou

GeoHazards 2026, 7(2), 40; https://doi.org/10.3390/geohazards7020040 - 7 Apr 2026

Abstract

Geohazards such as landslides, earthquakes, debris flows, and floods are governed by complex interactions among geological, hydrological, and human processes. Traditional data-driven models have improved hazard prediction but often lack interpretability and adaptability. This review examines the evolution of knowledge-guided approaches in geohazard [...] Read more.

Geohazards such as landslides, earthquakes, debris flows, and floods are governed by complex interactions among geological, hydrological, and human processes. Traditional data-driven models have improved hazard prediction but often lack interpretability and adaptability. This review examines the evolution of knowledge-guided approaches in geohazard research, highlighting how knowledge representation and artificial intelligence have progressively converged to enhance understanding, reasoning, and model transparency. A bibliometric analysis of 1410 publications indexed in the Web of Science reveals an evolution from early ontology-based knowledge engineering for expert reasoning to knowledge graphs (KG), frameworks enabling multi-source data integration and relational inference, and more recently, to large language model (LLM), augmented systems for automated knowledge extraction and cognitive geoscience. This review synthesizes advances in knowledge representation, knowledge graphs, and LLM-based reasoning, demonstrating how hybrid models that embed physical laws and expert knowledge can improve the interpretability and generalization of machine learning. These developments enable new forms of knowledge-driven geohazard intelligence and support applications in hazard monitoring, early warning, and risk communication. There are challenges we still face, including semantic fragmentation, limited causal reasoning, and sparse data for extreme events. Future directions require unified knowledge–data–mechanism architectures, causality-aware modeling, and interoperable standards to advance trustworthy and explainable geohazard intelligence. Full article

(This article belongs to the Topic Big Data and AI for Geoscience)

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24 pages, 9329 KB

Open AccessArticle

Mapping and Spatiotemporal Analysis of Landslides Along the Costa Viola Transportation Network (Italy)

by Massimo Conforti and Olga Petrucci

GeoHazards 2026, 7(2), 39; https://doi.org/10.3390/geohazards7020039 - 3 Apr 2026

Abstract

Rainfall-induced landslides represent one of the most recurrent geohazards affecting the transportation network of southwestern Calabria (Italy). This study provides an integrated assessment of landslide occurrence and road damage along the Costa Viola by combining detailed geomorphological mapping, multi-temporal analyses, historical documentation (1950–2025), [...] Read more.

Rainfall-induced landslides represent one of the most recurrent geohazards affecting the transportation network of southwestern Calabria (Italy). This study provides an integrated assessment of landslide occurrence and road damage along the Costa Viola by combining detailed geomorphological mapping, multi-temporal analyses, historical documentation (1950–2025), and GIS-based spatial data processing. A total of 261 landslides were mapped, affecting approximately 19% of the study area. Slides constitute the dominant movement type (66.7%), followed by complex landslides, flows, and falls. Landslide distribution is strongly controlled by geological and morphometric factors: more than 80% of mapped phenomena occur in highly fractured granitic and gneissic rocks, over 70% lie within 500 m of faults, and more than 90% are located within 300 m of streams. Slope gradient (25–55°) and local relief (350–550 m) further contribute to slope instability patterns. The historical dataset documents 237 landslide-induced road damage events over 75 years, with a marked increase in occurrence since the early 2000s. Most damage events affected the SS18 road and frequently corresponded to reactivations of pre-existing landslides, highlighting the long-term persistence of slope instability and the seasonal influence of intense autumn–winter precipitation. Overall, the results demonstrate that landslide hazard in the Costa Viola is governed by the interplay between structural, lithological, geomorphic, and climatic factors, compounded by anthropogenic modifications along road corridors. The combined landslide inventory and historical database provide a robust basis for risk mitigation, identification of critical road sectors, and future susceptibility and predictive modelling to support effective territorial planning. Full article

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14 pages, 4982 KB

Open AccessArticle

Fault Structure Characterization in the Gulf of Evia (Central Greece): Insights from an Enhanced, Relocated Seismic Catalog (2018–2023)

by Andreas Karakonstantis, Vasilis Kapetanidis, Nikolaos Madonis, Haralambos Kranis and George Kaviris

GeoHazards 2026, 7(2), 38; https://doi.org/10.3390/geohazards7020038 - 31 Mar 2026

Abstract

We present an enhanced earthquake catalog for Central Evia and the Northern Gulf of Evia, in Central Greece, between June 2018 and November 2023. The area is characterized by a low background seismicity rate, with occasional clustered events and seismic swarms, including those [...] Read more.

We present an enhanced earthquake catalog for Central Evia and the Northern Gulf of Evia, in Central Greece, between June 2018 and November 2023. The area is characterized by a low background seismicity rate, with occasional clustered events and seismic swarms, including those of February–April 2022 near Drosia and of October 2022 near Styra. The seismic catalog was enhanced by integrating additional data acquired through the application of the EQ-Transformer deep-learning model. A total of ~1400 events were analyzed, with ~1200 of them being successfully relocated with the double-difference method. The available focal mechanisms indicate predominantly normal, oblique-normal, and pure strike-slip faulting. The relocated seismicity was examined in conjunction with known mapped faults to investigate the activated structures at depth, providing insight into their degree of activity. In Drosia, the seismicity, at a depth of ~14 km, can be related to an E–W dextral strike-slip fault, with subtle surficial expression. In Psachna, the epicenters are oriented in an NE–SW direction, not matching the strike of the mainshock’s normal focal mechanism, but roughly coinciding with NE–SW-oriented topographic spurs and the local drainage pattern. In Markates and Prokopi, the seismicity is sparse, but the focal mechanisms are consistent with SW–NE dextral strike-slip faulting, aligned with the trend of the Nileas depression and the Prokopi–Pelion fault zone. Finally, in Mouriki, the seismic cluster is characterized by WNW-ESE normal faulting, most likely related to the SSW-dipping Messapio fault. Full article

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19 pages, 2051 KB

Open AccessReview

Assessing Coastal Exposure Index to Sea Level Rise Along North Java’s Coastline with the InVEST Model: A Critical Case Study from Regency of Jepara to Semarang City, Indonesia

by Muhammad Rizki Nandika, Herlambang Aulia Rachman, Martiwi Diah Setiawati, Abd. Rahman As-syakur, Atika Kumala Dewi, La Ode Alifatri, Tri Atmaja, Takahiro Osawa and A. A. Md. Ananda Putra Suardana

GeoHazards 2026, 7(2), 37; https://doi.org/10.3390/geohazards7020037 - 26 Mar 2026

Abstract

Utilizing the InVEST coastal exposure model and multi-source geospatial data, this study evaluates coastal vulnerability to sea-level rise along a critical stretch of the North Coast of Central Java, Indonesia, specifically focusing on the Semarang, Demak, and Jepara regions. A Coastal Exposure Index [...] Read more.

Utilizing the InVEST coastal exposure model and multi-source geospatial data, this study evaluates coastal vulnerability to sea-level rise along a critical stretch of the North Coast of Central Java, Indonesia, specifically focusing on the Semarang, Demak, and Jepara regions. A Coastal Exposure Index (CEI) was constructed for 256.63 km of shoreline by integrating key environmental variables, including wave climate, high-resolution coastal topography, shoreline geomorphology, bathymetry, coastal habitat distribution, and observed sea-level rise trends-based satellite altimetry from AVISO. The CEI classified coastal segments into five risk categories from Very Low to Very High exposure. A comparative analysis was performed between a scenario incorporating coastal habitats and a scenario without habitats to determine the protective role of natural ecosystems. The results of the analysis show that the average sea-level rise in the study area is 4.3 mm/year. Moreover, the findings also show that the inclusion of coastal habitats significantly reduces extreme exposure levels. Without accounting for habitats, 22.8% of the coastline was classified as Very High exposure, whereas with habitats included this portion dropped to 1.8%. For example, in Jepara Regency the length of shoreline in Very High exposure class decreased from 53.7% (no habitat scenario) to 5.5% when habitats were considered. Overall, the presence of coastal ecosystems shifted large stretches of the coast to lower exposure classes. This study demonstrates that natural habitats have a critical influence on coastal exposure, substantially mitigating the vulnerability of North Java’s coastline to sea-level rise. Full article

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24 pages, 11247 KB

Open AccessArticle

Machine Learning Analysis of Landslide Susceptibility in the Western Québec Seismic Zone of Canada

by Kevin Potoczny, Katsuichiro Goda and Abouzar Sadrekarimi

GeoHazards 2026, 7(1), 36; https://doi.org/10.3390/geohazards7010036 - 11 Mar 2026

Abstract

Landslide hazard potential is high across the St. Lawrence lowlands of Québec, Canada, due to sensitive glaciomarine clay deposits and the presence of moderate seismic activity, causing slope failures in the region. The main objectives of the study are to develop a working [...] Read more.

Landslide hazard potential is high across the St. Lawrence lowlands of Québec, Canada, due to sensitive glaciomarine clay deposits and the presence of moderate seismic activity, causing slope failures in the region. The main objectives of the study are to develop a working database for landslides in the region and use that database to improve regional landslide susceptibility analysis. Using high-resolution (1 m by 1 m cells) digital terrain models dated from 2009 and validated with satellite photogrammetry from 2012, a landslide inventory of 263 cases related to the 2010 Val-des-Bois earthquake (moment magnitude 5.0) is created. Relationships between landslide susceptibility factors, such as slope angle, and seismic conditioning factors, such as peak ground acceleration, are examined through machine learning methods. For landslide detection, an overall accuracy of approximately 85% (AUC 0.914) is achieved using random forest and logistic regression models cross-validated through 5-fold analysis, showing improvement over the currently employed Hazus method, which achieves an accuracy of approximately 67%. From a regional perspective, the developed inventory and resultant susceptibility models are unique and form the foundation for future studies to improve the understanding of earthquake-induced landslides in the Western Québec Seismic Zone, which historically lacks detailed landslide inventories. Full article

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25 pages, 3570 KB

Open AccessArticle

A Context-Aware Flood Warning Framework Integrating Ensemble Learning and LLMs

by Adnan Ahmed Abi Sen, Fares Hamad Aljohani, Nour Mahmoud Bahbouh, Adel Ben Mnaouer, Omar Tayan and Ahmad. B. Alkhodre

GeoHazards 2026, 7(1), 35; https://doi.org/10.3390/geohazards7010035 - 11 Mar 2026

Abstract

Smart cities require effective disaster management (like flooding, solar storms, sandstorms, or hurricanes), as it directly impacts people’s lives. The key challenges of disaster management are timely detection and effective notification during the crisis. This research presents a smart multi-layer framework for notification [...] Read more.

Smart cities require effective disaster management (like flooding, solar storms, sandstorms, or hurricanes), as it directly impacts people’s lives. The key challenges of disaster management are timely detection and effective notification during the crisis. This research presents a smart multi-layer framework for notification classification and management before and during flooding disasters. The framework includes an early detection module as the main phase in the alerting process. This step depends on an Ensemble Learning (EL) model based on a triad of the three best selected models (Deep Learning (DL), Random Forest (RF), and K-nearest Neighbor (KNN)) to analyze data collected continuously from the Internet of Things (IoT) layer. In the boosting phase, the framework utilizes Large Language Models (LLMs) with DL to analyze social textual crowdsourcing data. The results will enable the framework to identify the most affected areas during a flood. The framework adds a fog computing layer alongside a cloud layer to enable instantaneous processing of user responses and generate specialized alerts based on contextual factors such as location, time, risk level, alert type, and user characteristics. Through testing and implementation, the proposed algorithms demonstrated an accuracy rate of over 98% in detecting threats using a dataset of real, collected weather and flooding data. Additionally, the framework proposes a centralized control panel and a design of a smartphone application that offers essential services and facilitates communication among managed civil defense teams, citizens, and volunteers. Full article

(This article belongs to the Topic AI for Natural Disasters Detection, Prediction and Modeling)

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36 pages, 14302 KB

Open AccessArticle

Effect of Earthquake and Hydrostatic Water Pressure on the Seismic Stability of Slopes Supported by Mechanically Stabilized Earth Retaining Walls

by Zeinab Bayati, Arash K. Pour, Ali Saeidi and Ehsan Noroozinejad Farsangi

GeoHazards 2026, 7(1), 34; https://doi.org/10.3390/geohazards7010034 - 4 Mar 2026

Abstract

This study evaluates the stability of slopes supported by mechanically stabilized earth walls under combined hydrostatic and seismic loading conditions. Limit equilibrium, pseudo-static methods and permanent displacement approaches, including the Newmark rigid block method as well as coupled and decoupled techniques, are employed [...] Read more.

This study evaluates the stability of slopes supported by mechanically stabilized earth walls under combined hydrostatic and seismic loading conditions. Limit equilibrium, pseudo-static methods and permanent displacement approaches, including the Newmark rigid block method as well as coupled and decoupled techniques, are employed to assess the static and seismic performance of the soil slope under investigation. Parametric analyses are conducted using the Slide2 software package (Version 9.041) and verified against geotechnical design criteria to examine the effects of groundwater level and seismic intensity on factors of safety, failure mechanisms, and seismic-induced displacements of the slope. Results based on multiple strong ground motion records indicate that elevated water tables and hydrostatic pressures behind the wall, up to levels near the wall toe, do not significantly increase the failure potential or slope displacement. This behavior is attributed to the MSE wall acting as a rigid stabilizing system that enhances overall slope stability. Full article

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30 pages, 7149 KB

Open AccessArticle

Volcanic Hazard Assessment of a Monogenetic Volcanic Field with Sporadic and Limited Information: Deterministic Approach for Harrat Lunayyir, Saudi Arabia

by Károly Németh, Abdulrahman Sowaigh, Mahmoud Ashor, Mostafa Toni and Vladimir Sokolov

GeoHazards 2026, 7(1), 33; https://doi.org/10.3390/geohazards7010033 - 4 Mar 2026

Abstract

Saudi Arabia is experiencing interactions between ongoing urbanization, tourism growth, infrastructure projects in western regions along the Red Sea, and volcanic hazards. The area contains extensive monogenetic volcanic fields with hundreds of volcanoes formed during the Quaternary period. The large scale of the [...] Read more.

Saudi Arabia is experiencing interactions between ongoing urbanization, tourism growth, infrastructure projects in western regions along the Red Sea, and volcanic hazards. The area contains extensive monogenetic volcanic fields with hundreds of volcanoes formed during the Quaternary period. The large scale of the region often limits and fragments volcanological research, resulting in insufficient age and chemical data to understand the spatial and temporal development of many volcanic fields. Increased tourism has created a need for volcanic hazard assessments, particularly since some volcanic fields are considered possible tourist destinations. Harrat Lunayyir, in northwestern Saudi Arabia, is an example where such assessments have been conducted. Hazard assessments seek to provide information about potential future eruption types, locations, and impacts over timeframes relevant to urban planning and risk management. Due to rapid local development, these assessments may be required on short notice for specific small areas within larger volcanic fields, even when geological data are limited. This report presents a deterministic, scenario-based method for addressing such requests in the Lunayyir Volcanic Field. Results indicate a young Holocene eruption site characterized by a complex scoria cone associated with lava spattering, Strombolian, violent Strombolian activity and extensive transitional-type lava effusion. Full article

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30 pages, 11497 KB

Open AccessArticle

Strong Ground Motion Scenarios of the 1953 Disastrous Earthquake (M7.2) in Cephalonia, Greece

by Ioannis Grendas and Nikolaos Theodoulidis

GeoHazards 2026, 7(1), 32; https://doi.org/10.3390/geohazards7010032 - 4 Mar 2026

Abstract

In the 20th century, several large-magnitude earthquakes (M > 7.0) occurred in Greece and surrounding areas, some of which caused extensive structural damage and significant loss of life. Unfortunately, for these earthquakes, there was no recorded ground motion intensity data to extract information [...] Read more.

In the 20th century, several large-magnitude earthquakes (M > 7.0) occurred in Greece and surrounding areas, some of which caused extensive structural damage and significant loss of life. Unfortunately, for these earthquakes, there was no recorded ground motion intensity data to extract information about the macroseismic intensity distribution within the affected areas. A characteristic example of such an earthquake is the M7.2 of 12 August 1953 on Cephalonia island, which led to the almost complete destruction of settlements across the Cephalonia, Zakynthos, and Ithaca islands in western Greece. Although the vulnerability of the buildings affected in 1953 substantially differs from modern structures, the intensity and spatial extent of the shaking indicate that an event of similar magnitude could, even today, place the built environment and critical infrastructure of the region at high seismic risk. This study aims to estimate peak ground acceleration and velocity (PGA–PGV) and macroseismic intensity for the Cephalonia, Zakynthos, and Ithaca islands associated with earthquake scenarios comparable to the 1953 event (M7.2), incorporating seismotectonic information about active faults linked to the historical earthquake and considering associated uncertainties. Ground motion prediction models recently developed for Greece are employed. High PGA values (0.41–0.44 g) are estimated for the M7.2 earthquake, for rock site conditions (Vs30

\geq

790 m/s), covering almost the entire island of Cephalonia; these can be considered as the minimum values expected on rock site conditions for a similar earthquake scenario. Full article

(This article belongs to the Collection Geohazard Characterization, Modeling, and Risk Assessment)

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