基于地貌、气象和水文因素的城市洪水发生及影响空间分析方法

Q3 Social Sciences Human Geographies Pub Date : 2022-08-29 DOI:10.3390/geographies2030031
E. Feloni, A. Anayiotos, E. Baltas
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引用次数: 2

摘要

城市洪水会对城市的基础设施和财产造成重大损失,造成人员伤亡,扰乱人类活动,并对人民和地方政府行政部门造成其他问题和负面影响。本研究的目的是探讨城市洪水发生与潜在洪水触发因素的关系。分析是在雅典盆地西部(希腊阿提卡)进行的,在过去的几十年里,那里发生了几次洪水事件,造成了人员损失和财产和基础设施的破坏。洪水的影响是通过市民向消防应急热线求助的次数来衡量的,而潜在的影响因素是该地区的若干地貌特征和有关风暴的水文气象指数,这些因素是借助GIS技术确定的。分析是基于二元逻辑回归和广义线性回归模型的研究,这些模型用于建立潜在洪水影响因素与洪水发生/影响之间的关系。整个分析强调了由于考虑不同因素、事件以及分析中使用的网格的不同单元大小而导致的变化。结果表明,与描述洪水影响的模型相比,二元logistic回归模型对洪水发生具有更高的可预测性。
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A Spatial Analysis Approach for Urban Flood Occurrence and Flood Impact Based on Geomorphological, Meteorological, and Hydrological Factors
Urban flooding can cause significant infrastructure and property damage to cities, loss of human life, disruption of human activities, and other problems and negative consequences on people and the local government administration. The objective of this research work is to investigate the relation between urban flood occurrence and potentially flood-triggering factors. The analysis is performed in the western part of Athens Basin (Attica, Greece), where over the past decades several flood events caused human losses and damages to properties and infrastructure. Flood impact is measured by the number of citizen calls for help to the emergency line of the fire service, while potentially influencing factors are several geomorphological characteristics of the area and hydrometeorological indices regarding storms, which were determined with the aid of GIS techniques. The analysis is based on the investigation on binary logistic regression and generalized linear regression models that are used to build relationships between the potentially flood-influencing factors and the flood occurrence/impact for three events that were selected for reasons of comparison. The entire analysis highlights the variations attributed to the consideration of different factors, events, as well as to the different cell size of the grid used in the analysis. Results indicate that, the binary logistic regression model performed for flood occurrence achieves higher predictability, compared to the ability of the model used to describe flood impact.
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来源期刊
Human Geographies
Human Geographies Social Sciences-Geography, Planning and Development
CiteScore
1.10
自引率
0.00%
发文量
7
审稿时长
8 weeks
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