采用dematel、SAW方法和DPSIR模型进行密延多布洪水风险制图

M. Safaripour, Nafiseh Rezapour Andabili
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引用次数: 2

摘要

评估洪水风险绘图对可持续发展至关重要。目前的研究旨在确定伊朗Miyandoab地区洪水的原因并预测造成的破坏程度。采用驱动、压力、状态、影响、响应(DPSIR)概念框架模型对该地区洪水影响因素进行了分析。DPSIR是一种确定人类与环境之间关键关系的系统方法,它与简单相加加权(SAW)模型相结合,确定了实现可持续发展的新战略。采用DPSIR方法对该地区进行洪水易感性分析,考察了社会、经济、自然和环境因素作为驱动因素。然后,通过编制驱动力图和绘制区域地图,确定该区域的洪水风险等级。为此,采用决策试验与评价实验室(Dematel)和SAW模型研究各因素之间的因果关系,计算各层权重;采用Matlab软件对模型进行实现。最后,基于SAW方法提取的权重,在地理信息系统(GIS)环境下进行风险映射。结果表明,在研究区总面积中,高风险区约78,462公顷,中等风险区约91,542公顷,低风险区约2,952公顷。将决策支持系统模型与GIS模型相结合的结果表明,该模型在确定洪水高风险区域方面具有较高的效率。
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Miyandoab flood risk mapping using dematel and SAW methods and DPSIR model
It is essential to assess flood risk mapping for sustainable development. The present study aimed to identify the causes of flooding and predict the extent of damage caused in the area of Miyandoab, Iran.  The Driver, Pressure, State, Impacts, response (DPSIR) conceptual framework model was used to analyze the factors affecting flooding in the region. DPSIR is a system approach that identifies key relationships between humans and the environment, and its combination with the simple additive weighting (SAW) model identifies a new strategy for achieving sustainable development. The DPSIR method for flood susceptibility analysis in the region, examined the social, economic, physical, and environmental factors as the driving force. The flood risk level was then determined for the region by preparing the driving force map and mapping the region. For this purpose, the decision-making trial and evaluation laboratory (Dematel) and SAW models were used to investigate the causal relationship between the factors and calculate the weight of layers; Matlab software was used to implement the models. Finally, based on the weights extracted from the SAW method, risk mapping was performed in the geographic information system (GIS) environment. The results showed that out of the total area of the study area, about 78,462 hectares have a high risk, 91,542 hectares have medium risk, and 2,952 hectares have a low risk of flood. The results from combining the models of decision support systems and GIS indicated high efficiency in determining the areas with a high risk of flood.
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