Natural Hazards Research最新文献_第6页

Natural hazard induced coastal vulnerability in Indian Sundarbans: A village-level study by using geospatial and statistical techniques 印度孙德尔本斯地区自然灾害导致的沿海脆弱性：基于地理空间和统计技术的村级研究

Natural Hazards Research

Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2024.10.004

Md Hasnine, Dewaram Abhiman Nagdeve

The Sundarbans, an area with a history dating back to the dawn of civilization, has faced numerous environmental hazards that have remarkably affected the lives and livelihoods of its natives. Strom surge and Tropical cyclones are the most substantial natural hazards, causing severe damage to local communities by affecting food security, the economy, shelter and health. By defining vulnerability as a function of exposure, sensitivity, and resilience capacity, we calculated a composite vulnerability index (CVI) using equal weight method (EWM) to assess the vulnerability of mouzas (small administrative units) to natural hazards in the Sundarbans, India. The vulnerability map has been drawn based on composite value of CVI which shows 30.50% of villages falling into the high vulnerability category and 12.06% in the very high vulnerability category. The mouza-level analysis also indicates that 22.62% of Sundarbans's villages are highly exposed to natural hazards and 19.70 % of villages are classified as being at Very high sensitive. Only 7.07% villages have very high adopted capacity against these natural hazards. Villages in the southern parts and along the coast were found to be more vulnerable to storm surges. Conversely, those situated at higher elevations in the central area exhibited lower vulnerability. In the northern part of the region, several villages faced high to very high vulnerability due to low-lying, waterlogged wetlands. This study offers vital insights for decision-makers, government planners, and disaster management professionals, assisting in the identification of high-risk populations and areas that require immediate preservation efforts.

孙德尔本斯是一个历史可以追溯到文明之初的地区，它面临着许多环境危害，这些危害极大地影响了当地人的生活和生计。风暴和热带气旋是最严重的自然灾害，通过影响粮食安全、经济、住房和健康，对当地社区造成严重破坏。通过将脆弱性定义为暴露、敏感性和恢复能力的函数，我们采用等权重法（EWM）计算了综合脆弱性指数（CVI），以评估印度孙德尔本斯地区mouzas（小行政单位）对自然灾害的脆弱性。根据CVI综合值绘制的脆弱性地图显示，30.50%的村庄属于高脆弱性，12.06%的村庄属于极高脆弱性。蚊级分析还表明，孙德尔本斯22.62%的村庄高度暴露于自然灾害，19.70%的村庄被列为非常高敏感。只有7.07%的村庄对这些自然灾害具有很高的应对能力。南部和沿海地区的村庄更容易受到风暴潮的影响。相反，位于中部高海拔地区的脆弱性较低。在该地区的北部，由于地势低洼，湿地浸水，几个村庄面临着高度或非常高的脆弱性。这项研究为决策者、政府规划者和灾害管理专业人员提供了重要的见解，有助于确定需要立即采取保护措施的高风险人群和地区。

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引用次数: 0

Lightweight CNN model for automatic detection and depth estimation of subsurface voids using GPR B-scan data 使用GPR b扫描数据自动探测和估计地下空洞深度的轻量级CNN模型

Natural Hazards Research

Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2025.02.001

Abdelaziz Mojahid , Driss EL Ouai , Khalid EL Amraoui , Khalil EL-Hami , Hamou Aitbenamer , Jochem Verrelst , Pier Matteo Barone

Subsurface cavities pose significant risks, including structural instability, safety hazards, and environmental damage. Early detection of these cavities is crucial to prevent material losses and protect human lives. Investigation and manual processing of these structures using traditional methods can be difficult and time-consuming. Therefore, automated approaches using machine learning algorithms for identifying subsurface anomalies have recently emerged, providing promising pathways for real-time cavity detection. Consequently, this study proposes a Convolutional Neural Network (CNN)-based framework for the automated detection and depth estimation of subsurface cavities from Ground Penetrating Radar (GPR) B-scan images. The model was trained on 1408 augmented B-scans collected with 200 and 400 MHz antennas across various subsurface materials, ensuring exposure to a wide range of material types with different electromagnetic properties. Testing experiments were performed using eight profiles where cavity detection was confirmed by borehole data. The results demonstrate an impressive 100% success rate for cavity detection and over 95% accuracy in depth estimation. Comparing this model to other deep learning-based methods, our results show great remarkable performance tested in various subsurface environments. Furthermore, the model's lightweight design can be deployed on normal portable computing machines, enabling real-time cavity detection and depth estimation during the acquisition. The proposed approach in this study provides practical solutions that can have a significant impact in civil engineering applications, providing an efficient and reliable tool for subsurface challenging problems.

地下空腔构成了巨大的风险，包括结构不稳定、安全隐患和环境破坏。早期发现这些蛀牙对于防止物质损失和保护人类生命至关重要。使用传统方法对这些结构进行调查和手工处理既困难又耗时。因此，最近出现了使用机器学习算法来识别地下异常的自动化方法，为实时空腔检测提供了有希望的途径。因此，本研究提出了一个基于卷积神经网络（CNN）的框架，用于从探地雷达（GPR） b扫描图像中自动检测和深度估计地下空洞。该模型在1408次增强b扫描上进行了训练，这些增强b扫描由200和400 MHz天线收集，覆盖各种地下材料，确保暴露于具有不同电磁特性的各种材料类型。利用8个剖面进行了测试实验，通过钻孔数据证实了空腔探测。结果表明，空腔检测成功率高达100%，深度估计准确率超过95%。将该模型与其他基于深度学习的方法进行比较，我们的结果在各种地下环境中测试了非常出色的性能。此外，该模型的轻量化设计可以部署在普通便携式计算机上，在采集过程中实现实时空腔检测和深度估计。本研究提出的方法提供了实际的解决方案，可以对土木工程应用产生重大影响，为地下挑战性问题提供了高效可靠的工具。

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引用次数: 0

Machine learning approaches for mapping and predicting landslide-prone areas in São Sebastião (Southeast Brazil) <s:1> o sebasti<e:1>（巴西东南部）滑坡易发地区测绘和预测的机器学习方法

Natural Hazards Research

Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2024.10.003

Enner Alcântara , Cheila Flávia Baião , Yasmim Carvalho Guimarães , José Roberto Mantovani , José Antonio Marengo

This study employs machine learning techniques to map and predict landslide-prone areas in São Sebastião, Brazil, a region susceptible to landslides due to its steep terrain and intense rainfall. We compared five algorithms: Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbors, using various environmental factors as inputs. The Gradient Boosting model performed best, achieving an AUC-ROC of 0.963 and an accuracy of 99.6%. Slope degree, soil moisture index, and relief dissection emerged as the most influential factors in predicting landslide susceptibility. Analysis of land use and land cover changes between 1985 and 2021 revealed significant increases in forest cover and urban areas, with implications for landslide risk distribution. The resulting susceptibility map shows predominantly low-risk areas with scattered high-risk zones, providing crucial information for targeted risk management. This research demonstrates the effectiveness of machine learning in landslide susceptibility mapping and offers valuable insights for disaster risk reduction and urban planning in coastal mountainous regions.

本研究采用机器学习技术来绘制和预测巴西 o sebasti地区的滑坡易发地区，该地区由于地形陡峭和强降雨而容易发生滑坡。我们比较了五种算法：随机森林、梯度增强、支持向量机、人工神经网络和k近邻，使用各种环境因素作为输入。梯度增强模型表现最好，AUC-ROC为0.963，准确率为99.6%。坡度、土壤水分指数和地形解剖是预测滑坡易感性的主要影响因素。对1985年至2021年间土地利用和土地覆盖变化的分析显示，森林覆盖和城市地区显著增加，这对滑坡风险分布产生了影响。由此得出的易感性图显示，低风险区为主，高风险区分散，为有针对性的风险管理提供了重要信息。该研究证明了机器学习在滑坡易感性测绘中的有效性，并为沿海山区的灾害风险降低和城市规划提供了有价值的见解。

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引用次数: 0

A massive landslide damaged NHPC's Teesta V hydropower station on 20th August 2024 at Balutar, Sikkim Himalayas 2024年8月20日，位于锡金喜马拉雅山Balutar的国家水电公司Teesta V水电站发生大规模滑坡

Natural Hazards Research

Pub Date : 2025-06-01 DOI: 10.1016/j.nhres.2024.12.005

Biswajit Bera

A gigantic landslide occurred at Dipu-Dara near Singtam of Himalayan State Sikkim above the powerhouse of the NHPC's (National Hydroelectric Power Corporation) Teesta stage V dam power station on August 20, 2024. The Gas Insulated Switchgear building of the powerhouse was partly smashed and this landslide also affected the six residential buildings. The large cracks developed along the Singtam-Dikchu road which is the significant lifeline of Gangtok Town as well as North Sikkim. This study attempts to identify the principal causes and probable effects at the proximity region. Here, the geotechnical investigation has been done for slope stability using the limit equilibrium method (LEM). A total of three slopes (rock-debris) have been considered and the physical properties of the slopes have been systematically measured (slope material, angle, orientation, height, etc.) during a field survey in September 2024. SAR (C-band) imageries (Synthetic aperture radar, Sentine-1A) have been used here for InSAR coherence analysis before (09-08-2024) and after the event (21-08-2024). Results showed that most of the slopes (above 45°) along the riverside of Teesta are characterized by unconsolidated loose soil-forming materials of Phyllitic rock. At the time of GLOF, 2023, the slopes near the powerhouse were affected by the devastating flood through toe erosion. Here, this rock type experiences alternating dry and wet cycles which weaken its mechanical strength, develop cracks, and trigger slope failure. This 17 km NHPC headrace tunnel runs through fragile phyllite, schist, slate and quartzite rocks and it reduces the rock strength. Numerous past earthquake epicenters (the highest 5.45 magnitude, 2013) are also located between Dikchu and Signtam. The result of the LEM showed that the safety factor value of the landslide slope was 1.069, representing a little stable slope. This study will help policymakers for long-term sustainable hillslope as well as landslide management, particularly for the Himalayan tourism industry and border security.

2024年8月20日，喜马拉雅锡金邦辛塔姆附近的迪普达拉（Dipu-Dara）发生了巨大的山体滑坡，发生在国家水电公司Teesta第五阶段大坝发电站的发电站上方。电厂燃气绝缘开关柜部分倒塌，6栋居民楼也受到影响。大裂缝沿着singam - dikchu公路发展，这条公路是Gangtok镇和北锡金的重要生命线。本研究试图确定在邻近地区的主要原因和可能的影响。本文采用极限平衡法（LEM）对边坡稳定性进行了岩土工程研究。在2024年9月的野外调查中，共考虑了三个边坡（岩屑），并系统地测量了边坡的物理性质（边坡材料、角度、方向、高度等）。本文使用SAR （c波段）图像（合成孔径雷达，sentinel - 1a）在事件发生前（09-08-2024）和事件发生后（21-08-2024）进行InSAR相干性分析。结果表明：蒂斯塔河畔45°以上的斜坡多以非固结松散成土材料为phylistic rock；在2023年的GLOF期间，厂房附近的斜坡受到毁灭性洪水的影响，通过脚趾侵蚀。在这里，这种岩石类型经历了干湿交替循环，这削弱了其机械强度，形成裂缝，并引发边坡破坏。这条17公里长的NHPC引水隧道穿过脆弱的千层岩、片岩、板岩和石英岩，降低了岩石的强度。许多过去的地震震中（2013年最高的5.45级）也位于Dikchu和Signtam之间。LEM计算结果表明，滑坡边坡的安全系数值为1.069，属于较稳定的边坡。这项研究将有助于决策者长期可持续的山坡和滑坡管理，特别是对喜马拉雅旅游业和边境安全。

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引用次数: 0

Assessment of gully erosion susceptibility using four data-driven models AHP, FR, RF and XGBoosting machine learning algorithms 基于AHP、FR、RF和XGBoosting机器学习算法的沟道侵蚀敏感性评估

Natural Hazards Research

Pub Date : 2025-03-01 DOI: 10.1016/j.nhres.2024.05.001

Md Hasanuzzaman , Pravat Shit

Gully erosion is a significant global threat to socioeconomic and environmental sustainability, making it a widespread natural hazard. Developing spatial models for gully erosion is crucial for local governance to effectively implement mitigation measures and promote regional development. This study applied two machine learning (ML) models, RF and XGB, alongside an AHP-based multi-criteria decision method and FR bivariate statistics, to assess gully erosion susceptibility (GES) in the Kangsabati River basin in eastern India's Chotonagpur plateau fringe. A GIS database was created, incorporating recorded gully erosion incidents and 20 conditioning variables, which were evaluated for multicollinearity. These variables served as predictive factors for assessing gully erosion presence in the study area. The models' performance was evaluated using metrics such as RMSE, MAE, specificity, sensitivity, and accuracy. The XGB model outperformed the others, achieving a predictive accuracy of 90.22%. The study found that approximately 6.56% of the Kangsabati catchment is highly susceptible to gully erosion, with 12.39% moderately susceptible and 81.05% not susceptible. The XGB model had the highest ROC value of 85.5 during testing, indicating its superiority over the FR (ROC = 81.7), AHP (ROC = 79.8), and RF (ROC = 83.8) models. These findings highlight the XGB model's efficacy and potential for large-scale GES mapping.

沟蚀是对社会经济和环境可持续性的重大全球性威胁，是一种广泛存在的自然灾害。建立沟壑侵蚀空间模型对于地方治理有效实施缓解措施和促进区域发展至关重要。本研究采用两种机器学习（ML）模型，RF和XGB，以及基于ahp的多标准决策方法和FR二元统计，评估了印度东部Chotonagpur高原边缘康萨巴蒂河流域的沟道侵蚀敏感性（GES）。建立了一个GIS数据库，将记录的沟壑侵蚀事件和20个条件变量纳入其中，并对其进行多重共线性评估。这些变量可作为评估研究区域沟蚀存在的预测因子。使用RMSE、MAE、特异性、敏感性和准确性等指标评估模型的性能。XGB模型优于其他模型，实现了90.22%的预测准确率。研究发现，康萨巴蒂流域约6.56%的流域高度易受沟蚀影响，12.39%的流域中度易受沟蚀影响，81.05%的流域不受沟蚀影响。XGB模型在检验中ROC值最高，为85.5，优于FR （ROC = 81.7）、AHP （ROC = 79.8）和RF （ROC = 83.8）模型。这些发现突出了XGB模型的有效性和大规模GES映射的潜力。

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引用次数: 0

Geospatial intelligence for landslide susceptibility and risk analysis: Insights from NH31A and east Sikkim Himalaya settlements 滑坡易感性和风险分析的地理空间情报：来自NH31A和东锡金喜马拉雅定居点的见解

Natural Hazards Research

Pub Date : 2025-03-01 DOI: 10.1016/j.nhres.2024.10.001

Sk Asraful Alam , Sujit Mandal , Ramkrishna Maiti

Slope instability is a serious concern in the Sikkim Himalayas. The town and numerous road segments along National Highway 31A were ravaged by multiple landslides that occurred in the nearby region. A bivariate statistical method known as frequency ratio (FR), information value (IV), and certainty factor (CF) analysis was employed in this work to examine landslide risk assessment (LRA) and landslide susceptibility zonation (LSZ) maps in the Rorachu watershed. This study represents the first comprehensive analysis of landslide risk in the populated areas of East Sikkim and along NH31A, offering a deeper understanding of the risks involved and contributing to the enhancement of local resilience against landslide hazards. A total of 153 different landslide locations were mapped using Google Earth and GIS software; 30% (46) of these locations were used to validate the models, and 70% of these (107) served as training data for the FR, IV, and CF models. The thirteen landslide causative factors (geology, soil, elevations, slope, curvature, drainage density (DD), road density (RD), rainfall, normalized difference vegetation index (NDVI), land use land cover (LULC), topographic position index (TPI), stream power index (SPI), and topographic wetness index (TWI)) were extracted from a spatial database for LSZ mapping. Landslides were most prevalent on slopes (35°–50°), heights (2500–4100 m), and rainfall (2000–2500 mm and 3000–3300 mm). The area under the curves (AUC) for the FR, IV, and CF models are 0.925 (92.50%), 0.846 (84.60%), and 0.868 (86.20%), respectively. The prediction rates are shown by the AUCs for the FR, IV, and CF models, which are 0.828 (82.8%), 0.750 (%), and 0.836 (83.60%), respectively. According to the landslide risk assessment (LRA), the FR (20.75%), IV (40.91%) and CF (18.78%) models showed high risk on Highway 31A, while the FR (9.05%), IV (38.59%) and CF (20.90%) models showed high risk in densely populated areas. These landslide risk and vulnerability maps can be used to develop land use planning strategies that can save lives and are useful for planners and mitigation measures. Special attention should be paid to urbanization, highway construction, and deforestation.

斜坡失稳是锡金喜马拉雅地区的一个严重问题。附近地区发生的多次山体滑坡破坏了该镇和31A国道沿线的许多路段。采用双变量统计方法，即频率比（FR）、信息值（IV）和确定性因子（CF）分析，对罗罗楚流域的滑坡风险评估（LRA）和滑坡易感性区划（LSZ）图进行了检验。该研究首次对东锡金人口稠密地区和NH31A沿线的滑坡风险进行了全面分析，对所涉及的风险有了更深入的了解，并有助于提高当地对滑坡灾害的抵御能力。利用谷歌Earth和GIS软件绘制了153个不同的滑坡位置；这些位置中的30%（46个）用于验证模型，其中70%（107个）作为FR、IV和CF模型的训练数据。从空间数据库中提取13个滑坡成因因子(地质、土壤、高程、坡度、曲率、排水密度（DD）、道路密度（RD）、降雨、归一化植被差指数（NDVI）、土地利用土地覆盖（LULC）、地形位置指数（TPI）、河流功率指数（SPI）和地形湿度指数（TWI），用于LSZ制图。滑坡在坡度（35°-50°）、高度（2500-4100米）和降雨量（2000-2500毫米和3000-3300毫米）最常见。FR、IV和CF模型的曲线下面积（AUC）分别为0.925（92.50%）、0.846（84.60%）和0.868（86.20%）。FR、IV和CF模型的auc预测率分别为0.828（82.8%）、0.750（%）和0.836（83.60%）。根据滑坡风险评价（LRA），在31A高速公路上FR（20.75%）、IV（40.91%）和CF（18.78%）模型的危险性较高，而在人口密集地区FR（9.05%）、IV（38.59%）和CF（20.90%）模型的危险性较高。这些滑坡风险和脆弱性地图可用于制定土地使用规划战略，这些战略可以挽救生命，并有助于规划人员和采取缓解措施。应特别注意城市化、公路建设和森林砍伐。

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引用次数: 0

Advances in the study of natural disasters induced by the "23.7" extreme rainfall event in North China 华北“23.7”极端降雨事件诱发的自然灾害研究进展

Natural Hazards Research

Pub Date : 2025-03-01 DOI: 10.1016/j.nhres.2025.01.003

Chenchen Xie , Chong Xu , Yuandong Huang , Jielin Liu , Xiaoyi Shao , Xiwei Xu , Huiran Gao , Junxue Ma , Zikang Xiao

The article compiles and summarizes research on the natural disasters triggered by the "23.7" extreme rainfall in North China, utilizing the VOSViewer (Visualization of Similarities viewer) software for bibliometric analysis of existing studies and looking forward to future research. Based on the CNKI(China National Knowledge Infrastructure) database, the article focuses on studies related to meteorological, hydrological, and geological disasters resulting from this event. A total of 145 documents were obtained, including 41 articles on meteorological disaster research, 77 articles on hydrological disaster research, and 12 articles on geological disaster research. In the study of meteorological disasters, analyses of the causes and characteristics of heavy rainstorms have been summarized. In the study of hydrological disasters, analyses of the causes and impacts of floods have been summarized. In the study of geological disasters, analyses of the causes of disasters, case studies, and research on monitoring and early warning have been summarized. This event clearly demonstrates a continuous process from meteorological events to hydrological impacts, and then to geological disasters, forming a distinct "meteorological-hydrological-geological disaster" chain. Due to the increasing impact of climate change and human activities on extreme rainfall events, future research should delve deeper into the roles of these factors in floods and geological disasters, strengthen flood disaster management, and enhance geological disaster early warning systems. This is essential for reducing disaster losses and safeguarding the safety of people's lives and property.

本文对华北地区“23.7”极端降雨引发的自然灾害研究进行了梳理和总结，利用VOSViewer （Visualization of similarity viewer）软件对已有研究进行文献计量分析，并对未来的研究进行展望。本文以中国知网数据库为基础，重点研究了该事件导致的气象、水文和地质灾害。共获得文献145篇，其中气象灾害研究论文41篇，水文灾害研究论文77篇，地质灾害研究论文12篇。在气象灾害研究中，对暴雨的成因和特征进行了总结分析。在水文灾害的研究中，主要对洪水的成因和影响进行了分析。在地质灾害研究中，对灾害成因分析、案例研究和监测预警研究进行了总结。该事件清晰地展示了一个从气象事件到水文影响再到地质灾害的连续过程，形成了一个独特的“气象-水文-地质灾害”链。由于气候变化和人类活动对极端降雨事件的影响越来越大，未来的研究应深入研究这些因素在洪水和地质灾害中的作用，加强洪水灾害管理，完善地质灾害预警系统。这对减少灾害损失、保障人民生命财产安全至关重要。

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引用次数: 0

Site response analysis for estimation of seismic site amplification in the city of Durban (South Africa) 南非德班地震场地放大估计的场地反应分析

Natural Hazards Research

Pub Date : 2025-03-01 DOI: 10.1016/j.nhres.2024.11.002

B. Manzunzu , V. Midzi , B. Zulu , T. Mulabisana , T. Pule , M. Sethobya , N. Mankayi

The city of Durban has previously experienced higher than expected ground motions from large distant earthquakes. It is potentially exposed to significant seismic hazard due to seismic site amplification, which needs to be estimated for effective mitigation efforts. Detailed stochastic one dimensional (1D) seismic site response analyses were performed at 90 sites in the city. Analytical models have demonstrated that they can simulate reasonably well the seismic ground motions amplification. The most widely used model is the equivalent linear approach. The approach computes the ground response of horizontally layered soil deposits subjected to transient and vertically propagating shear waves through a 1D soil column. Seven earthquake time histories together with developed sub-surface models were selected as input parameters to estimate the seismic site amplification at the 90 sites in the city. The used time histories were taken from the 2014 M5.5 Orkney earthquake with distance range (4.8–46.9 km). The uncertainties in ground motion input, variation in the shear wave velocity and variations in the shear modulus reduction and damping curves (i.e. variation of non-linear properties) were carefully modelled. Results obtained from this study were used to prepare maps of peak ground acceleration (PGA) at the surface and amplification factors. The minimum and maximum PGA at surface are estimated as 0.01 g and 0.30 g respectively. Based on the results of the analysis, the city may sustain amplification in the range of 0.7–4.7 at PGA with high values along the coast. The results indicate that the low shear wave velocity values, weak and soft material at shallow depths are responsible for the higher amplifications observed especially along the coast. Therefore, a site-specific design approach should be adopted for the seismic design of critical structures.

德班市以前经历过比预期更高的遥远地震引起的地面运动。由于地震场地的放大，它可能面临重大的地震危险，需要对其进行估计，以进行有效的减灾工作。详细的随机一维（1D）地震现场反应分析进行了90个站点的城市。分析模型表明，该模型能较好地模拟地震地震动的放大。最广泛使用的模型是等效线性方法。该方法通过一维土柱计算水平层状土沉积物在瞬态和垂直传播的剪切波作用下的地面响应。选取7次地震时程和已开发的地下模型作为输入参数，对城市90个站点的地震场放大进行了估计。使用的时间历史取自2014年奥克尼岛5.5级地震，距离范围（4.8-46.9公里）。对地震动输入的不确定性、剪切波速的变化、剪切模量折减和阻尼曲线的变化（即非线性特性的变化）进行了仔细的模拟。研究结果用于制备地表峰值地加速度（PGA）图和放大系数图。地表PGA最小值为0.01 g，最大值为0.30 g。分析结果表明，在沿海高值的PGA区，我市可能在0.7 ~ 4.7范围内持续放大。结果表明，较低的横波速度值和浅层的弱软物质是观测到的较高振幅的原因，特别是沿海地区。因此，关键结构的抗震设计应采用因地制宜的设计方法。

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引用次数: 0

Atami landslides 2021 Japan: Landfill issues, elderly casualties, key lessons and challenges 2021年日本热海滑坡：垃圾填埋场问题、老年人伤亡、关键教训和挑战

Natural Hazards Research

Pub Date : 2025-03-01 DOI: 10.1016/j.nhres.2024.06.006

Namita Poudel , Guo Chi, Cao Yuqiu, Rajib Shaw

Landslides are a common problem worldwide, significantly impacting human societies. Japan is particularly susceptible to multiple hazards, including landslides. The Atami landslide in 2021 raised concerns about Japan's disaster management and evacuation processes. In this context, this research aims to compare the Atami landslide with previous landslides occurring between 2013 and 2021, focusing on their causes and impacts, particularly on elderly people. A comparative method is used to analyze two or more similar types of disasters. To accomplish the objectives of the paper, pertinent reports, government papers, and articles are reviewed. The findings indicate that the Atami landslide was distinct due to secondary causes, specifically illegal landfill management, where the landfill's height was increased beyond permissible limits. During the monsoon season, heavy rainfall led to flash floods in Atami city, resulting in human casualties and property loss. The study also found that the number of elderly casualties was high, similar to previous landslides, highlighting deficiencies in the evacuation system. The research suggests implementing a combined digital and community network-based early warning system and immediate follow-up inspections of other landfill sites as additional measures to improve existing disaster management strategies for future preparedness.

山体滑坡是全球普遍存在的问题，对人类社会产生了重大影响。日本特别容易受到多种灾害的影响，包括山体滑坡。2021年的热海山体滑坡引发了人们对日本灾害管理和疏散过程的担忧。在此背景下，本研究旨在将热海滑坡与2013年至2021年间发生的其他滑坡进行比较，重点研究其原因和影响，特别是对老年人的影响。比较方法用于分析两种或两种以上相似类型的灾害。为了完成论文的目标，相关的报告，政府文件和文章进行审查。研究结果表明，热海滑坡的明显特征是由于次要原因，特别是非法填埋管理，填埋高度超过了允许的范围。在季风季节，暴雨导致热海市发生山洪暴发，造成人员伤亡和财产损失。该研究还发现，老年人伤亡人数很高，与之前的山体滑坡类似，凸显了疏散系统的缺陷。该研究建议实施一个基于数字和社区网络的联合早期预警系统，并对其他垃圾填埋场进行立即的后续检查，作为改进现有灾害管理战略的额外措施，以便将来做好准备。

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引用次数: 0

Improving social resilience to forest fire from community perspective 从社区角度提高社会对森林火灾的抵御能力

Natural Hazards Research

Pub Date : 2025-03-01 DOI: 10.1016/j.nhres.2024.08.004

Yafang Wen, Ariyaningsih, Chi Guo, Anuska Ray, Rajib Shaw

Recently, terms like social and community resilience have provided new ideas in reducing disaster risks especially in forest fire. However, a comprehensive and in-depth review of community social resilience concerning forest fires is lacking. There is little research investigate whether certain social or community resilience factors can initiate forest fires or whether forest fire prevention positively be influenced by them. To fill this gap, this paper aims to identify and discuss the factors influencing the occurrence of forest fires in the scope of community social resilience. It also provides recommendations for forest fire prevention and enhancing community social resilience to forest fires. PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) framework were used to do the systematic review. The results show that there are 4 main factors concerning the social resilience to forest fire such as, social capital, forest fire cultural, community economic, and community characteristics. In addition, this research also suggests future recommendations for preventing forest fires and improving community resilience to forest fires.

最近，社会和社区恢复力等术语为减少灾害风险，特别是森林火灾风险提供了新的思路。然而，缺乏对森林火灾的社区社会恢复力的全面和深入的审查。关于某些社会或社区恢复力因素是否会引发森林火灾，以及这些因素是否会对森林防火产生积极影响的研究很少。为了填补这一空白，本文旨在识别和讨论社区社会恢复力范围内影响森林火灾发生的因素。它还为森林防火和提高社区对森林火灾的抵御能力提供了建议。采用PRISMA（系统评价和荟萃分析首选报告项目）框架进行系统评价。结果表明，社会资本、森林火灾文化、社区经济和社区特征是影响森林火灾社会恢复力的主要因素。此外，本研究还为未来预防森林火灾和提高社区对森林火灾的抵御能力提出了建议。

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引用次数: 0