Natural Hazards Research最新文献

Application of limit equilibrium and shear strength reduction techniques for stability assessment of slope cuts- a case study of khalid-Dijo dam project, southern Ethiopia 极限平衡与抗剪强度折减法在坡面稳定性评价中的应用——以埃塞俄比亚南部khalid-Dijo大坝工程为例

Natural Hazards Research Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2024.10.005

Demeke Wendim , Mamaru Genetu

{"title":"Application of limit equilibrium and shear strength reduction techniques for stability assessment of slope cuts- a case study of khalid-Dijo dam project, southern Ethiopia","authors":"Demeke Wendim , Mamaru Genetu","doi":"10.1016/j.nhres.2024.10.005","DOIUrl":"10.1016/j.nhres.2024.10.005","url":null,"abstract":"<div><div>Dam failure can occur due to foundation instability, downstream and upstream slopes instabilities. This study assesses the stability of upstream and downstream slope cuts at Khalid-Dijo irrigation dam project, which is located in Southern Ethiopia, 3 km south of Werabe town. Limit equilibrium and finite element shear strength reduction methods are adopted. Validation of results and comparisons between those methods are carried out. The analysis considers anticipated site conditions, including static dry, static saturated, dynamic dry and dynamic saturated conditions. Slope material properties are measured from insitu, laboratory tests and used as input parameters for the analysis to obtain factor of safety and critical strength reduction factors. The properties considered in the analysis include unit weight, cohesion, angle of internal friction, poison's ratio, dilation angle and Young's modulus. The analysis indicates that the factor of safety values for limit equilibrium methods and the critical strength reduction factor for finite element method are very similar across the three slope cuts under all anticipated conditions. The lowest factor of safety and critical strength reduction factor is 1.56 and 2.07 respectively. Generally, the proposed dam project is safe against upstream and downstream slope failures. These studies suggest that maintained the average safety factor values of both methods during the design stage are crucial to avoid unnecessary risk.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 276-286"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Post-disaster flooded region segmentation using DeepLabv3+ and unmanned aerial system imagery 利用DeepLabv3+和无人机系统图像对灾后洪涝地区进行分割

Natural Hazards Research Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2024.12.003

Akila Agnes Sundaresan, Appadurai Arun Solomon

{"title":"Post-disaster flooded region segmentation using DeepLabv3+ and unmanned aerial system imagery","authors":"Akila Agnes Sundaresan, Appadurai Arun Solomon","doi":"10.1016/j.nhres.2024.12.003","DOIUrl":"10.1016/j.nhres.2024.12.003","url":null,"abstract":"<div><div>Natural disasters, particularly floods, have become increasingly frequent and intense in recent times, posing significant threats to human lives and infrastructure, especially in developing countries. Efficient flood detection and damage assessment are critical for effective disaster response and recovery. This study applies the DeepLabv3+ model with UAS imagery to achieve precise flood area delineation. The DeepLabv3+ model employs an encoder-decoder architecture, integrating Atrous Spatial Pyramid Pooling (ASPP) and atrous convolutions to capture multi-scale contextual features while preserving spatial details. To evaluate its performance, the study experiments with various backbone architectures, including ResNet-18, ResNet-50, MobileNetV2, and Xception, under different configurations of downsampling rates (8 and 16) and atrous rates (8, 12, and 16). ResNet-50 proves to be the most effective backbone, achieving the optimal balance between segmentation accuracy and computational efficiency. The ASPP module enhances global and local feature extraction, while the decoder combines low-level spatial and high-level semantic features for precise pixel-wise segmentation. Experimental results reveal that the DeepLabv3+ model significantly enhances the detection of flooded regions and the delineation of flood extents, providing a reliable tool for real-time disaster management and contributing to improved flood management practices. This research offers valuable insights into leveraging deep learning models for enhanced disaster response in regions where rapid and accurate flood detection is crucial.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 363-371"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence and numerical weather prediction models: A technical survey 人工智能与数值天气预报模式：技术综述

Natural Hazards Research Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2024.11.004

Muhammad Waqas , Usa Wannasingha Humphries , Bunthid Chueasa , Angkool Wangwongchai

{"title":"Artificial intelligence and numerical weather prediction models: A technical survey","authors":"Muhammad Waqas , Usa Wannasingha Humphries , Bunthid Chueasa , Angkool Wangwongchai","doi":"10.1016/j.nhres.2024.11.004","DOIUrl":"10.1016/j.nhres.2024.11.004","url":null,"abstract":"<div><div>Can artificial intelligence (AI) models beat traditional numerical weather prediction (NWP) models based on physical principles? The rapid advancement of AI, inherent computational limitations of NWP models, and the lack of access to big data drive this question in terms of resolution and complexity. This survey offers a systematic review of studies that integrate AI with NWP models at various stages of weather and climate modeling. It aims to address key research questions, including the types of forecasting models, the integration of AI into NWP systems, and the comparative efficacy of AI-based approaches versus conventional NWP models. It covered peer-reviewed literature from 2000 to 2024. This technical survey highlights key advancements in the application of AI within NWP modeling in data assimilation, augmentation, pre-processing, adaptive parameter tuning, optimization, uncertainty quantification, extreme event prediction, post-processing, and the interpretation of NWP outputs. While AI demonstrates significant potential in post-processing NWP outputs, pre-processing remains challenging. This survey also presents state-of-the-art AI-based hybrid models and assesses their applicability to weather data. It highlights the promise of AI in potentially replacing traditional NWP models but emphasizes the need for further advancements in model development and application. The study also offers a detailed classification of forecasting models and outlines promising directions for future research.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 306-320"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data 坦桑尼亚乞力马扎罗山增强森林火灾预测的深度学习模型：整合卫星图像、天气数据和人类活动数据

Natural Hazards Research Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2024.12.001

Cesilia Mambile, Shubi Kaijage, Judith Leo

{"title":"Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data","authors":"Cesilia Mambile, Shubi Kaijage, Judith Leo","doi":"10.1016/j.nhres.2024.12.001","DOIUrl":"10.1016/j.nhres.2024.12.001","url":null,"abstract":"<div><div>Forest fires (FFs) are a growing threat to ecosystems and human settlements, particularly in vulnerable regions such as Mount Kilimanjaro, Tanzania. Accurate and timely fire prediction is essential to mitigate these risks and improve fire management strategies. This study develops and evaluates advanced Deep Learning (DL) models for FF prediction by integrating spatiotemporal vegetation indices, environmental data, and human activity indicators. Specifically, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), and Convolutional Long Short-Term Memory (ConvLSTM) models were employed to analyze Sentinel-2 satellite imagery and weather data, along with anthropogenic factors such as beekeeping, tourism, agriculture, and deforestation rates. Leveraging this diverse, high-dimensional dataset, the ConvLSTM model engineered to capture intricate spatial and temporal relationships delivered superior performance, achieving an AUROC of 0.9785 and Accuracy 98.08%, surpassing the LSTM and CNN models. Integrating human-induced activities with environmental data, these models provide accurate and actionable predictions for fire management in high-risk areas. This study demonstrates the potential of ConvLSTM in developing operational tools for early fire detection, streamlining data-driven decision-making, improving resource allocation, and guiding preventive strategies in fire-prone regions such as Mount Kilimanjaro.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 335-347"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Surface creep differences and affecting factor along the Xianshuihe fault zone in response to the Wenchuan and Lushan earthquakes 汶川、芦山地震后鲜水河断裂带地表蠕变差异及其影响因素

Natural Hazards Research Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2024.12.008

Guanzhong Liu , Aichun Liu , Qin Su , Lianglong Fan , Ao Song , Aipeng Guo , Yanxing Li

{"title":"Surface creep differences and affecting factor along the Xianshuihe fault zone in response to the Wenchuan and Lushan earthquakes","authors":"Guanzhong Liu , Aichun Liu , Qin Su , Lianglong Fan , Ao Song , Aipeng Guo , Yanxing Li","doi":"10.1016/j.nhres.2024.12.008","DOIUrl":"10.1016/j.nhres.2024.12.008","url":null,"abstract":"<div><div>As the southwest and southeast boundaries of the Bayan Har block, the Xianshuihe Fault Zone (XSHFZ) and the Longmenshan Fault Zone (LMSFZ) are likely to be intrinsically related in terms of tectonic activity. Consequently, it is pertinent to consider the potential response of fault creep in the XSHFZ when the LMSFZ encounters different major earthquake disturbances. This paper presents a comparative and analytical study of the fault creep characteristics of the XSHFZ, employing cross-fault short-baseline survey data collected prior to and following the Wenchuan M8.0 and Lushan M7.0 earthquakes. The findings indicate that despite the Wenchuan earthquake having a significantly higher magnitude than the Lushan earthquake and the theoretical static stress change being considerably larger than that of the Lushan earthquake, the absolute velocity and incremental change of fault creep in the XSHFZ before and after the Wenchuan earthquake are less pronounced than those observed before and after the Lushan earthquake. Following the Wenchuan earthquake, the creep rate and nature of the XSHFZ remained largely unchanged, with a slight decrease in the average slip rate. However, in the aftermath of the Lushan earthquake, the XSHFZ exhibited clear signs of \"activation,\" with a notable increase in the average creep rate and the emergence of strong tensional motion. It is hypothesized that the Maerkang, Miyaluo, Minshan, and Huya Fault Zones within the Longmenshan sub-block contributed to the increased complexity of the strong earthquake process, potentially acting as a 'regulating valve' in influencing the redistribution of post-seismic stress and the creep pattern of adjacent faults. Consequently, their roles in regional tectonic activity cannot be overlooked.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 391-398"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flood hazard zones prediction using machine-learning-based geospatial approach in lower Niger River basin, Nigeria 基于机器学习的地理空间方法在尼日利亚尼日尔河下游流域的洪水危险区预测

Natural Hazards Research Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2025.01.002

Adedoyin Benson Adeyemi, Akinola Adesuji Komolafe

{"title":"Flood hazard zones prediction using machine-learning-based geospatial approach in lower Niger River basin, Nigeria","authors":"Adedoyin Benson Adeyemi, Akinola Adesuji Komolafe","doi":"10.1016/j.nhres.2025.01.002","DOIUrl":"10.1016/j.nhres.2025.01.002","url":null,"abstract":"<div><div>Flooding has had devastating impacts on lives and properties over the years, caused as a result of climate change, rapid population growth, urbanization, and poor urban planning. The recurring events of this hazard necessitate the development of accurate flood hazard maps to better inform disaster preparedness and mitigation strategies. Therefore, this study aims to integrate Machine Learning Models (MLM) with Geographic Information Systems (GIS) techniques to predict flood hazard zones in the lower Niger River basin in Nigeria. The Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN) machine learning models were employed to assess flood-prone areas based on twenty (20) influencing factors, categorized into topographic, hydrologic, environmental/anthropogenic, and climatic factors. Based on historical flood events from different sources for the period of 1998–2023 within the study area, data from 1164 flooded and non-flooded points were utilized to train and test the models. Following the evaluation by statistical metrics such as precision, recall, f1-score, overall accuracy, and Receiver Operating Characteristics Area Under the Curve (ROC-AUC), XGBoost was found to have the best performance with an overall accuracy of 91% and ROC-AUC score of 0.89 compared to SVM and ANN with overall accuracy 88% and 85% respectively, and ROC-AUC scores 0.82 and 0.86 respectively. The flood hazard maps showed that areas near the river, particularly in the central and southern part of the basin, including the river confluence areas, are most prone to flooding which is likely to affect critical elements such as croplands, settlements, population centers, and infrastructures. This study provides a foundation to prioritize efforts and resources toward mitigating flood impacts in highly vulnerable areas.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 399-412"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seismic capacity assessment of Shikhara style temple with and without traditional seismic enhancement 采用和不采用传统抗震加固的释宫式寺庙抗震能力评价

Natural Hazards Research Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2024.11.003

Suraj Malla , Mukil Alagirisamy , Purushotam Dangol

{"title":"Seismic capacity assessment of Shikhara style temple with and without traditional seismic enhancement","authors":"Suraj Malla , Mukil Alagirisamy , Purushotam Dangol","doi":"10.1016/j.nhres.2024.11.003","DOIUrl":"10.1016/j.nhres.2024.11.003","url":null,"abstract":"<div><div>Brick masonry heritage structures have long been understood as highly vulnerable under earthquake excitation. Due to preservation constraints and considerations, modern materials are not allowed in seismic strengthening or post-earthquake reconstruction of heritage structures. Thus, traditional strengthening techniques are mandatory in heritage structures. The present paper reports the outcome of reconstruction initiative in a Shikhara style temple located in a world heritage site using timber element based seismic enhancement. Using numerical modeling, capacity assessment with and without timber elements is performed. For capacity assessment pushover analysis is performed for both as built and reconstructed models. The results show considerable increase in seismic capacity even with traditional improvement techniques, i.e. vertical timber elements. 18.05% increase in seismic capacity is found when vertical timber elements are used. Also, about 8% increase in the fundamental vibration frequency is observed after strengthening. The paper reports the details of geometrical features, reconstruction aspects, and numerical analysis results comprising modal analysis and capacity assessment.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 299-305"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in earthquake and cascading disasters 地震和级联灾害的研究进展

Natural Hazards Research Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2025.01.010

Xiangli He , Zhaoning Chen , Qing Yang , Chong Xu

{"title":"Advances in earthquake and cascading disasters","authors":"Xiangli He , Zhaoning Chen , Qing Yang , Chong Xu","doi":"10.1016/j.nhres.2025.01.010","DOIUrl":"10.1016/j.nhres.2025.01.010","url":null,"abstract":"<div><div>Earthquakes and cascading disasters have garnered increasing attention. The 2023 Annual Academic Conference of the Committee on Earthquake Hazard Chain, the Seismological Society of China, was successfully held in Beijing. The conference presentations and proceedings covered a wide range of topics beyond seismic disasters, including landslides, ground fissures, avalanches, freeze-thaw processes, land subsidence, dam-break floods, and multiple chain disasters, such as earthquake-landslide, rainfall-landslide-barrier lakes, earthquake-fires, and earthquake-tsunamis. The research directions addressed various aspects, such as disaster database development, formation mechanisms, spatial distribution, identification techniques, prediction methods, monitoring and early warning systems, hazard assessment, mitigation strategies, and post-disaster recovery and reconstruction. In this paper, we systematically categorize and review the conference presentations, focusing on the research status and recent advancements in three major areas: seismic disasters, earthquake-induced cascading disasters, and non-seismically triggered landslides. It aims to provide scientific references for theoretical studies and technological applications in disaster prevention, mitigation, and emergency response. In the future, we should integrate cutting-edge multidisciplinary technologies and theories, leverage data-driven approaches to build a comprehensive database of earthquake disaster chains, enhance precursor monitoring to improve accuracy, advance mechanism research, and develop robust risk assessment systems.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 421-431"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Shirur landslide of July 2024 triggered by intense rainfall and unchecked development 2024年7月的施鲁尔滑坡是由强降雨和不受控制的开发引发的

Natural Hazards Research Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2025.01.005

Priyajit Kundu , Varun Menon , Sreevalsa Kolathayar , Pruthviraj U

{"title":"The Shirur landslide of July 2024 triggered by intense rainfall and unchecked development","authors":"Priyajit Kundu , Varun Menon , Sreevalsa Kolathayar , Pruthviraj U","doi":"10.1016/j.nhres.2025.01.005","DOIUrl":"10.1016/j.nhres.2025.01.005","url":null,"abstract":"<div><div>On the morning of July 16, 2024, a significant landslide occurred in Shirur of Uttara Kannada district, Karnataka, India. The landslide claimed seven lives, leaving one person missing and severely disrupting the transport network by blocking National Highway 66. The displaced debris travelled 180m across the highway and into the Gangavali River, causing a significant splash and damaging structures on the opposite bank. The event, characterised by a rotational slip, was triggered by a combination of anthropogenic activities and intense rainfall. The construction of National Highway 66, which involved the removal of the slope's toe without adequate protection for the excavation, significantly destabilised the area. On 15th July, the rain gauge in Ankola recorded rainfall of 260 mm. The accumulated rainfall calculated for Shirur using Inverse Distance Weightage (IDW) for the storm period of 4 days was 198 mm, which increased the pore water pressure within the soil, weakening its shear strength and leading to slope failure. This incident underscores the need for further analysis and the implementation of appropriate mitigation measures, as the region remains at risk for future landslides. The Shirur landslide serves as a critical reminder of the dynamic nature of such disasters, particularly when human activities exacerbate natural hazards.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 413-420"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Soil liquefaction potential assessment of ports in Odisha, India 印度奥里萨邦港口土壤液化潜力评价

Natural Hazards Research Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2024.11.001

Satyaprakash Mishra, Arjun Sil, Amit Kumar Das

{"title":"Soil liquefaction potential assessment of ports in Odisha, India","authors":"Satyaprakash Mishra, Arjun Sil, Amit Kumar Das","doi":"10.1016/j.nhres.2024.11.001","DOIUrl":"10.1016/j.nhres.2024.11.001","url":null,"abstract":"<div><div>This study presents a comprehensive assessment of the liquefaction potential at three major port sites in Odisha, India, i.e., Dhamara, Paradeep, and Gopalpur, utilizing well-established methodologies. The evaluation was conducted under the framework of seismic hazard analysis, incorporating site-specific geotechnical data from Standard Penetration Test (SPT) boreholes and considering the maximum estimated earthquake scenarios. The seismic hazard analysis identified varying levels of Peak Ground Acceleration (PGA) across the port sites, with corresponding magnitudes of 7.4 and 6.5 for Dhamara, Paradeep, and Gopalpur, respectively. Liquefaction susceptibility was quantified using the Liquefaction Potential Index (LPI), revealing significant variability across the sites. The Gopalpur port site exhibited a very low to low liquefaction severity, indicating minimal risk under the assessed seismic conditions. In contrast, the Dhamara and Paradeep ports demonstrated a higher susceptibility, with two out of four borehole locations at each site showing high liquefaction severity. This elevated risk is primarily attributed to the presence of saturated silty fine sands in the upper soil layers, which are particularly prone to liquefaction. The findings underscore the critical need for targeted mitigation measures, including ground improvement techniques, at the Dhamara and Paradeep sites to enhance infrastructure stability and safety. The study provides valuable insights into the seismic risks associated with these coastal regions and offers a robust framework for future seismic risk assessments. The outcomes of this research are instrumental in informing the design and implementation of resilient infrastructure in Odisha, thereby contributing to the region's overall seismic safety and disaster preparedness.</div></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"5 2","pages":"Pages 287-298"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0