Abstract
Metro systems have become high-risk entities due to the increased frequency and severity of urban flooding. Therefore, understanding the flood risk of metro systems is a prerequisite for mega-cities’ flood protection and risk management. This study proposes a method for accurately assessing the flood risk of metro systems based on an improved trapezoidal fuzzy analytic hierarchy process (AHP). We applied this method to assess the flood risk of 14 lines and 268 stations of the Guangzhou Metro. The risk results validation showed that the accuracy of the improved trapezoidal fuzzy AHP (90% match) outperformed the traditional trapezoidal AHP (70% match). The distribution of different flood risk levels in Guangzhou metro lines exhibited a polarization signature. About 69% (155 km2) of very high and high risk zones were concentrated in central urban areas (Yuexiu, Liwan, Tianhe, and Haizhu); the three metro lines with the highest overall risk level were lines 3, 6, and 5; and the metro stations at very high risk were mainly located on metro lines 6, 3, 5, 1, and 2. Based on fieldwork, we suggest raising exits, installing watertight doors, and using early warning strategies to resist metro floods. This study can provide scientific data for decision-makers to reasonably allocate flood prevention resources, which is significant in reducing flood losses and promoting Guangzhou’s sustainable development.
Highlights
Published: 18 December 2021Floods cause billions of dollars of damage each year [1,2,3]
To adequately account for the majority of the experts’ ratings, we proposed the synthetic importance index of indicators, which is expressed in Equation (4): SI = ∑1 x ·r x where SI represents the indicator’s synthetic importance index; x indicates all scores appearing in the rating interval for an indicator and is integers belonging to 1–9; and rx refers to the ratio of the number of expert votes at score x to the number of valid votes
The regional flood risk level was eventually generated based on the normalized indicators indices and weights obtained using the improved trapezoidal fuzzy analytic hierarchy process (AHP)
Summary
Published: 18 December 2021Floods cause billions of dollars of damage each year [1,2,3]. Between 1980 and 2013, the global direct economic losses from floods exceeded $1 trillion (2013 values), and more than 220,000 people lost their lives [4]. Urban areas have faced global flood risk challenges due to extreme weather, rapid urbanization, and climate change, which has been increasing both in severity and frequency worldwide, increasing risks to human lives, health, properties, infrastructure, and the environment [5,6]. Developing countries face more severe flood challenges than developed countries, especially Chinese cities undergoing rapid urbanization [9]. It is indispensable to understand how to live with urban floods and mitigate flood risk for sustainable development in cities [10]
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