Progress in Disaster Science最新文献_第10页

Drought vulnerability assessment and mitigation strategies for peri-urban province of Pathum Thani, Thailand 泰国巴吞他尼省城郊地区干旱脆弱性评估和缓解战略

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-05-05 DOI: 10.1016/j.pdisas.2025.100431

Panita Saguansap , Prinya Mruksirisuk , Duangporn Garshasbi , Nawhath Thanwiset Thanvisitthpon

This study assesses the drought vulnerability of Thailand's peri-urban province of Pathum Thani using a three-component vulnerability assessment framework, comprising drought exposure, drought sensitivity, and drought adaptive capacity components. Pathum Thani province, consisting of seven administrative districts, is home to a number of industries including agriculture, manufacturing, and tourism. Rapid urbanization and climate change have exacerbated the province's drought situations. To assess the drought vulnerability of Pathum Thani, drought vulnerability indicators structured around the three vulnerability components are developed across the three sustainability dimensions: social, economic, and environmental dimensions. The drought vulnerability indicators are initially evaluated by experts for their relevancy. The drought indicators are further evaluated using a questionnaire administered to randomly selected households across seven administrative districts. The drought vulnerability components and indicators, based on the questionnaire responses, are subsequently validated by using structural equation modeling and confirmatory factor analysis. After the validation, a drought vulnerability questionnaire is developed to evaluate the drought vulnerability of the study area, measured by the province- and district-level drought vulnerability indexes. The research findings reveal a moderate level of drought vulnerability across most administrative districts. As a result, policymakers should focus interventions and mitigation strategies on reducing drought exposure, cultivating drought resilience, and enhancing adaptive capacity.

本研究采用三要素脆弱性评估框架，包括干旱暴露、干旱敏感性和干旱适应能力，对泰国巴吞他尼省的干旱脆弱性进行了评估。巴吞他尼省由七个行政区组成，是农业、制造业和旅游业等众多产业的所在地。快速的城市化和气候变化加剧了该省的干旱状况。为了评估Pathum Thani的干旱脆弱性，围绕三个脆弱性组成部分构建了干旱脆弱性指标，涵盖了三个可持续性维度：社会、经济和环境维度。干旱脆弱性指标由专家初步评估其相关性。通过对七个行政区域随机选择的家庭进行问卷调查，进一步评估了干旱指标。基于问卷调查结果，采用结构方程模型和验证性因子分析对干旱易损性成分和指标进行验证。验证后，编制干旱易损性问卷，采用省级和区级干旱易损性指数对研究区干旱易损性进行评价。研究结果显示，大多数行政区域的干旱脆弱性处于中等水平。因此，决策者应将干预措施和缓解战略的重点放在减少干旱暴露、培养抗旱能力和增强适应能力上。

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引用次数: 0

Ensemble weather-runoff forecasting models for reliable flood early warning systems 可靠洪水预警系统的综合天气径流预报模型

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-04-02 DOI: 10.1016/j.pdisas.2025.100420

Alberto de la Fuente , Carolina Meruane , Viviana Meruane

Flood early warning systems often rely on a single hydro-meteorological forecast, which can limit reliability. Recent advances in deep learning (DL) offer promising improvements due to their low computational cost, enabling the generation of ensemble forecasts. This study investigates how to process multiple weather-runoff forecasts to improve model performance in predicting extreme events. We applied DL-based weather-runoff forecasting in river stations located at the foot of the Andes Mountains in Chile. The models couple a near-future global weather forecast with short-range runoff forecasting systems based on Long Short-Term Memory (LSTM) cells. Meteorological and geomorphological input variables commonly used in hydrological models were selected. Training and validation used ERA5 data, while NCEP-GFS data were used for testing and real-time operation. Model performance was evaluated using the Kling-Gupta efficiency (0.6–0.8) and Nash-Sutcliffe efficiency (greater than 0.9). The threat score index, which assesses the model's ability to predict threat peak flow exceedance, ranged between 0.6 and 0.8. The best-performing models were analyzed probabilistically to quantify uncertainty. Finally, we introduced the concept of conditional probability to estimate the likelihood of exceeding a threat peak flow, providing a basis for raising alerts and improving decision-making under uncertain conditions.

洪水预警系统通常依赖于单一的水文气象预报，这可能会限制可靠性。深度学习（DL）的最新进展提供了有希望的改进，因为它们的计算成本低，能够生成集合预测。本研究探讨如何处理多个天气径流预报，以提高模型预测极端事件的性能。我们在智利安第斯山脉脚下的河流站应用了基于dl的天气径流预报。该模型将近期全球天气预报与基于长短期记忆（LSTM）单元的短期径流预报系统相结合。选取水文模型中常用的气象和地貌输入变量。训练和验证采用ERA5数据，测试和实时操作采用NCEP-GFS数据。采用Kling-Gupta效率（0.6-0.8）和Nash-Sutcliffe效率（大于0.9）对模型性能进行评价。威胁得分指数评估了模型预测威胁峰值流量超出的能力，范围在0.6到0.8之间。对表现最好的模型进行概率分析，以量化不确定性。最后，我们引入了条件概率的概念来估计超过威胁峰值流量的可能性，为不确定条件下的预警和改进决策提供了依据。

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引用次数: 0

Understanding the dynamics of evacuation delays: A study of the 2021 mount Semeru eruption through PLS-SEM analysis 了解疏散延迟的动力学：通过PLS-SEM分析对2021年塞梅鲁火山喷发的研究

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-04-01 DOI: 10.1016/j.pdisas.2025.100433

I Dewa Made Frendika Septanaya , Adjie Pamungkas , Anoraga Jatayu , Rivan Aji Wahyu Dyan Syafitri , Amien Widodo , Mayra Andrakayana

This study explores the key factors contributing to evacuation delays during the 2021 eruption of Mount Semeru in Indonesia. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), data were collected from 100 affected residents to examine behavioural responses and decision-making dynamics during the crisis. The analysis tested eight hypotheses and found that five were statistically significant, indicating that lack of information, emotional attachment to property, absence of evacuation plans, limited infrastructure, and family-related concerns were positively associated with delayed evacuation decisions. Notably, 71 % of respondents relied on neighbours as their primary information source, and 80 % reported experiencing panic during the evacuation process. These findings highlight critical gaps in community preparedness and emergency communication systems. The study concludes that strengthening early warning dissemination, enhancing infrastructure, conducting regular evacuation drills, and addressing socio-emotional factors are essential to improving evacuation effectiveness. This research contributes to a deeper understanding of evacuation behaviour during volcanic disasters and offers practical recommendations to enhance community resilience and emergency management practices.

本研究探讨了2021年印度尼西亚塞梅鲁火山喷发期间导致疏散延误的关键因素。利用偏最小二乘结构方程模型（PLS-SEM），收集了100名受影响居民的数据，以检查危机期间的行为反应和决策动态。该分析测试了8个假设，发现其中5个具有统计学意义，表明信息缺乏、对财产的情感依恋、缺乏疏散计划、基础设施有限以及家庭相关问题与延迟疏散决策呈正相关。值得注意的是，71%的受访者依赖邻居作为主要信息来源，80%的受访者报告在疏散过程中经历了恐慌。这些发现突出了社区准备和应急通信系统方面的重大差距。研究认为，加强预警传播、加强基础设施建设、定期进行疏散演练、处理社会情绪因素是提高疏散效果的关键。这项研究有助于更深入地了解火山灾害期间的疏散行为，并为加强社区复原力和应急管理实践提供实用建议。

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引用次数: 0

Disaster warning messages: challenges and opportunities based on Brazil's experience 灾害预警信息：基于巴西经验的挑战与机遇

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-04-01 DOI: 10.1016/j.pdisas.2025.100440

Murilo Noli da Fonseca, Luciene Pimentel da Silva

This research investigates the effectiveness of disaster communication messages in Brazil, with a focus on the structure and content of SMS messages sent out from 2018 to 2023. The Warning Response Model was used for coding. The analysis reveals that only 1.83 % of the 73,701 messages analyzed were complete; that is, they contain all the elements to be effective (source, hazard, location, guidance and time). The results also show that messages about natural hazards achieved the highest scores and that the states of Santa Catarina and Paraná stand out. In contrast, states such as Minas Gerais and Goiás achieved low scores regarding messages about technological risks. In addition, lacking geographical and time specifics, and failing to include details on the potential impacts compromise the effectiveness of the messages. The study highlights that the message length limit of 160 characters is an obstacle to effective communication, which can compromise the population's perception of risk, its adoption of protective actions. The research concludes that optimizing disaster communication messages is crucial to improve disaster response in Brazil, while it recommends using a multichannel system, the continuous training of civil defense agents, and greater community engagement to strengthen resilience.

本研究调查了巴西灾害通信短信的有效性，重点研究了2018年至2023年发送的短信的结构和内容。使用警告响应模型进行编码。分析显示，在被分析的73701条消息中，只有1.83%是完整的；也就是说，它们包含了所有有效的要素（来源、危害、位置、引导和时间）。调查结果还显示，有关自然灾害的信息得分最高，其中圣卡塔琳娜州和帕拉纳州尤为突出。相比之下，米纳斯吉拉斯州和Goiás等州在有关技术风险的信息方面得分较低。此外，缺乏具体的地理和时间，没有包括潜在影响的细节，损害了信息的有效性。该研究强调，160个字符的信息长度限制是有效沟通的一个障碍，这可能会损害人们对风险的感知，并采取保护措施。该研究的结论是，优化灾害通信信息对于改善巴西的灾害响应至关重要，同时它建议使用多渠道系统、持续培训民防人员以及加强社区参与以加强抗灾能力。

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引用次数: 0

On moving towards a more inclusive understanding of disaster risk reduction: A sexual and gender minorities perspective through the lens of global flood risk 关于对减少灾害风险有更包容的理解：从全球洪水风险的角度看性和性别少数群体的观点

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-04-01 DOI: 10.1016/j.pdisas.2025.100442

Eric Mortensen , Ana Clara Cassanti , Timothy Tiggeloven , Anne Twaalfhoven , Philip J. Ward , Toon Haer

Considering social vulnerability of marginalized communities is crucial for equitable disaster risk reduction. This paper emphasizes the need to include sexual and gender minorities in global vulnerability assessments and policymaking. This community faces unique challenges in disasters, often overlooked in disaster risk reduction strategies and agendas. While natural hazards do not discriminate, societal ideologies and laws can, amplifying disparate outcomes. Our flood risk analysis reveals that over one-third of the global expected annual affected population – 26.5 to 33.9 million people – live in countries lacking legal protections for sexual and gender minorities. Future scenarios indicate this could double to 58.6 to 73.1 million by 2050. Meanwhile, two-thirds of those at risk to floods reside in countries with below-average societal acceptance of sexual and gender minorities, increasing their vulnerability before, during and after flooding disasters. To address these disparities, global frameworks must urgently integrate specific metrics into social vulnerability assessments and risk planning. Including marginalized communities ensures that disaster risk reduction efforts are more responsive and effective. By acknowledging the intersection of societal acceptance, legal protections, and disaster risk, we can advance more inclusive and impactful strategies to mitigate the growing impacts of climate-fuelled hazards like coastal and riverine flooding.

考虑边缘化社区的社会脆弱性对于公平地减少灾害风险至关重要。本文强调了在全球脆弱性评估和政策制定中纳入性和性别少数群体的必要性。这个群体在灾害中面临着独特的挑战，而这些挑战在减少灾害风险的战略和议程中往往被忽视。虽然自然灾害不会造成歧视，但社会意识形态和法律会造成歧视，从而放大不同的后果。我们的洪水风险分析显示，全球预计每年受影响人口的三分之一以上（2650万至3390万人）生活在缺乏对性少数群体和性别少数群体的法律保护的国家。未来的情景表明，到2050年，这一数字可能会翻一番，达到5860万至7310万。与此同时，三分之二面临洪水风险的人居住在社会对性和性别少数群体的接受程度低于平均水平的国家，这增加了他们在洪水灾害发生之前、期间和之后的脆弱性。为了解决这些差异，全球框架必须紧急将具体指标纳入社会脆弱性评估和风险规划。包括边缘化社区，可确保减少灾害风险的努力更具响应性和有效性。通过认识到社会接受度、法律保护和灾害风险之间的相互作用，我们可以推进更具包容性和影响力的战略，以减轻沿海和河流洪水等气候引发的灾害日益严重的影响。

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引用次数: 0

Machine learning-based identification and assessment of snow disaster risks using multi-source data: Insights from Fukui prefecture, Japan 使用多源数据的基于机器学习的雪灾风险识别和评估：来自日本福井县的见解

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-04-01 DOI: 10.1016/j.pdisas.2025.100426

Zhenyu Yang , Hideomi Gokon , Qing Yu

Understanding the driving factors behind snowstorm risk and their nonlinear effects is critical for developing effective response strategies. This study, focusing on the 2018 Fukui snowstorm in Japan, integrates multi-source data, including mobile GPS data, Digital Elevation Model (DEM) data, road data, urban data, and traffic congestion data, to develop an interpretable model for quantifying high-risk areas and examining key nonlinear relationships and threshold effects influencing snowstorm impact occurrence, offering actionable insights for mitigation strategies. We employed four machine learning models—Decision Tree, Random Forest, Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost)—to capture complex nonlinear relationships among influencing factors and applied SHAP (SHapley Additive exPlanations) theory to interpret variable contributions. The results reveal that: (1) compared to Random Forest, Decision Tree, and MLP models, the XGBoost model demonstrates superior performance with a prediction accuracy of 0.8225; (2) factors such as elevation, slope, road density, and road width exhibit significant nonlinear impacts and threshold effects on snowstorm impact occurrence; (3) Urban areas with elevation below

51.9 m

, slopes exceeding

{9.9}^{°}

, a density of major roads (Road Type 1) less than

443.75 m / {km}^{2}

, a density of minor roads (Road Type 2) less than

550.25 m / {km}^{2}

, and where rural roads (Road Type 3) are nearly absent, along with population fluctuations ranging between

(- 0.25,0)

, are particularly vulnerable to snow disasters. In contrast, areas with flat terrain and high densities of rural roads are less likely to be affected; and (4) snow disaster resilience in mitigating traffic congestion can be improved by monitoring GPS data for early warnings and optimizing the sp. configuration of major and minor roads.

了解暴风雪风险背后的驱动因素及其非线性效应对于制定有效的应对策略至关重要。本研究以2018年日本福井雪灾为研究对象，整合多源数据，包括移动GPS数据、数字高程模型（DEM）数据、道路数据、城市数据和交通拥堵数据，建立可解释模型，量化高风险区域，研究影响雪灾影响发生的关键非线性关系和阈值效应，为减灾策略提供可操作的见解。我们采用了四种机器学习模型——决策树、随机森林、多层感知器（MLP）和极端梯度增强（XGBoost）——来捕捉影响因素之间复杂的非线性关系，并应用SHAP （SHapley加性解释）理论来解释变量贡献。结果表明：(1)与随机森林、决策树和MLP模型相比，XGBoost模型的预测精度为0.8225；(2)高程、坡度、道路密度、道路宽度等因子对暴雪影响的非线性影响和阈值效应显著；(3)高程低于51.9m、坡度超过9.9°、主要道路（道路类型1）密度小于443.75m/km2、次要道路（道路类型2）密度小于550.25m/km2、农村道路（道路类型3）几乎没有、人口波动范围在- 0.25 ~ 0之间的城市地区特别容易受到雪灾的影响。相比之下，地势平坦、乡村道路密度高的地区受影响的可能性较小；(4)通过监测GPS数据进行预警和优化主干道和次要道路的sp配置，可以提高缓解交通拥堵的积雪抗灾能力。

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引用次数: 0

Flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfall 考虑降雨统计特征的基于机器学习的城市洪水预测

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-04-01 DOI: 10.1016/j.pdisas.2025.100415

Se-Dong Jang, Jae-Hwan Yoo, Yeon-Su Lee, Byunghyun Kim

Urbanization has increased impervious surfaces, while climate change has intensified rainfall, leading to more frequent urban flooding. Traditional numerical models for flood prediction are accurate but time-consuming due to extensive parameter calibration and data processing. This study addresses these limitations by proposing a machine learning-based flood prediction method using a Random Forest model. By utilizing past rainfall data, 1D drainage system simulations, and 2D flood analyses, we trained the model to predict flood patterns for various rainfall events. To enhance prediction accuracy, statistical characteristics of rainfall, such as temporal distribution, were incorporated into the model. Performance metrics (RMSE, R², MAE) for the test dataset showed values of 3.1573, 0.9682, and 0.9484 for the total rainfall model, and 2.7354, 0.9761, and 0.8942 for the model with statistical characteristics. Both models displayed high predictive accuracy relative to the numerical model, with the Random Forest model using statistical characteristics showing slightly improved performance. This method provides faster, reliable flood predictions, offering a valuable tool for real-time urban flood management and decision-making during emergency situations.

城市化增加了不透水的地表，而气候变化加剧了降雨，导致城市洪水更加频繁。传统的洪水预报数值模型精度较高，但由于需要大量的参数校正和数据处理，因而耗时较长。本研究通过提出一种使用随机森林模型的基于机器学习的洪水预测方法来解决这些局限性。通过利用过去的降雨数据、一维排水系统模拟和二维洪水分析，我们训练了该模型来预测各种降雨事件的洪水模式。为了提高预测精度，模型中加入了降雨的时间分布等统计特征。测试数据集的性能指标（RMSE、R2、MAE）显示，全降雨模型的RMSE、R2和MAE分别为3.1573、0.9682和0.9484，统计特征模型的RMSE、R2和MAE分别为2.7354、0.9761和0.8942。与数值模型相比，两种模型都显示出较高的预测精度，使用统计特征的随机森林模型的性能略有提高。这种方法提供了更快、更可靠的洪水预测，为紧急情况下的实时城市洪水管理和决策提供了有价值的工具。

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引用次数: 0

Ground robot technologies in wildfire risk reduction. The viewpoint of the fire service 地面机器人技术在减少野火风险中的应用。消防部门的观点

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-04-01 DOI: 10.1016/j.pdisas.2025.100435

Pawel Gromek , Thomas Lowe

Background

Robots are not widely used in wildfire risk reduction. Firefighters do not commonly know how to use them and technology providers are not aware of key operational directions for improvements.

Objective

This study aims to identify, catalogue and discuss directions for the development of robot technologies in terms of wildfire risk reduction. The viewpoint of the fire service is presented.

Method

Survey was conducted with experts to gather new knowledge on the use of robots in wildfire response and to identify inspiration for improvements for technology providers.

Results

92 end-user-related developments were categorised into particular elements of wildfire response process. 31 development directions related to technology providers have been assigned to general robot functionalities: ensuring safety of firefighters, shaping situational awareness, and supporting firefighting systems. The robot functionality sets can be implemented in reconnaissance robots, delivery and evacuation robots, and firefighting robots.

Conclusion

Fire service perceives the robot use in wildfire risk reduction more broadly than is reflected by currently developed disaster robots and existing disaster risk reduction concepts. The viewpoint of the fire service can raise awareness among end-users and inspire technology providers to effectively and rationally implement robots for wildfire risk reduction.

机器人在减少野火风险方面的应用并不广泛。消防员通常不知道如何使用它们，技术提供商也不知道改进的关键操作方向。本研究旨在识别、分类和讨论机器人技术在减少野火风险方面的发展方向。提出了消防部门的观点。方法与专家一起进行调查，以收集有关在野火应对中使用机器人的新知识，并为技术提供商确定改进的灵感。结果92个与终端用户相关的发展被归类为野火响应过程的特定要素。与技术供应商相关的31个发展方向已被分配到一般机器人功能：确保消防员的安全，形成态势感知和支持消防系统。机器人功能集可以在侦察机器人、运送和疏散机器人以及消防机器人中实现。结论消防部门对机器人在减少野火风险中的应用的认识比目前开发的灾害机器人和现有的减灾概念所反映的要广泛。消防部门的观点可以提高最终用户的意识，并激励技术提供商有效合理地实施机器人来降低野火风险。

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引用次数: 0

Building damage assessment in natural disasters: A trans- and interdisciplinary approach combining domain knowledge, 3D machine learning, and crowdsourcing 自然灾害中的建筑损害评估：结合领域知识、3D机器学习和众包的跨学科和跨学科方法

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-04-01 DOI: 10.1016/j.pdisas.2025.100427

Julia Kohns , Vivien Zahs , Carolin Klonner , Bernhard Höfle , Lothar Stempniewski , Alexander Stark

Recent natural disasters have claimed many lives. Reliable damage predictions and timely assessments are essential for effective rescue operation planning and efficient allocation of limited resources. Currently, experts in the field perform damage assessment manually, which is resource- and time-intensive. To address this issue, we propose a general trans- and interdisciplinary concept that combines the strengths of domain knowledge, automated computational methods, and crowdsourcing. The objective is to provide relevant and timely damage information after a natural disaster. The specific implementation presented for the earthquake damage use case includes (1) the development of a set of novel, innovative methods, (2) their combination to obtain timely and reliable damage information, (3) fully defined interfaces between all components to ensure an automated data flow, (4) implementation as a fully open-source framework, and (5) the participation of end users in the development of the framework from the beginning, contributing their expertise. Compared to other existing individual solutions, our interdisciplinary implementation has shown to provide fast and accurate information in disaster situations, aiding the management of consequences and saving lives. We consider the implementation transferable to various types of natural hazards due to its open-source realisation and the flexibility of its modules and interfaces.

最近的自然灾害夺去了许多人的生命。可靠的灾害预测和及时的评估对于有效的救援行动规划和有效分配有限的资源至关重要。目前，专家在现场进行人工损伤评估，这是资源和时间密集型的。为了解决这个问题，我们提出了一个综合领域知识、自动化计算方法和众包优势的跨学科和跨学科概念。目标是在自然灾害发生后提供相关和及时的损害信息。针对地震损害用例提出的具体实现包括：(1)开发一套新颖、创新的方法；(2)将这些方法组合起来以获得及时、可靠的损害信息；(3)在所有组件之间完全定义的接口，以确保自动化数据流；(4)作为一个完全开源的框架实施；(5)终端用户从一开始就参与框架的开发，贡献他们的专业知识。与其他现有的单独解决方案相比，我们的跨学科实施已经证明可以在灾害情况下提供快速准确的信息，帮助管理后果并挽救生命。由于其开源实现以及模块和接口的灵活性，我们认为该实现可转移到各种类型的自然灾害中。

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引用次数: 0

Place and space tensions in post-disaster landscapes 灾后景观中的地点和空间紧张关系

IF 2.6 Q3 ENVIRONMENTAL SCIENCES

Progress in Disaster Science

Pub Date : 2025-04-01 DOI: 10.1016/j.pdisas.2025.100432

Muzayin Nazaruddin

Disasters caused by natural hazards and their subsequent recovery processes inevitably transform landscapes in varying degrees. This paper explores two Indonesian cases, the 2004 Indian Ocean Tsunami and the 2010 Mt. Merapi eruption, to show how post-disaster spatial arrangements often reflect a classic dichotomy of space and place. This is evident in post-disaster spatial categorisations and human settlements. However, these spatial stances are not mutually exclusive and can interact to form new hybrids. Post-disaster spatial categorisation is marked by tensions between the government's top-down disaster zoning and the local responses based on their daily sensory and bodily experiences. Post-disaster human settlements reflect a dynamic tension between restoring the former distribution of taskscapes and the sole focus on restoring spaces for living, which in turn leads to complex cultural changes and multiple-distracted landscapes. This analysis of post-disaster landscape change can inform post-disaster management by rethinking vulnerability and resilience and promoting a bottom-up approach alongside the common top-down approach practiced by the government.

自然灾害造成的灾害及其随后的恢复过程不可避免地在不同程度上改变景观。本文探讨了2004年印度洋海啸和2010年默拉皮火山喷发这两个印度尼西亚的案例，以展示灾后空间安排如何经常反映空间和地点的经典二分法。这在灾后空间分类和人类住区方面表现得很明显。然而，这些空间立场并不是相互排斥的，它们可以相互作用形成新的混合体。灾后空间分类的特点是政府自上而下的灾害分区与当地基于日常感官和身体体验的反应之间的紧张关系。灾后人类住区反映了一种动态的紧张关系，即恢复以前的任务景观分布和唯一的重点是恢复生活空间，这反过来又导致了复杂的文化变化和多重分心的景观。这种对灾后景观变化的分析可以为灾后管理提供信息，通过重新思考脆弱性和复原力，并在政府常用的自上而下的方法之外，推广自下而上的方法。

引用次数: 0