Assessing Urban Areas’ Vulnerability to Flood Disaster Based on Text Data: A Case Study in Zhengzhou City

Abstract

Confronting the frequent flood disasters triggered by torrential downpour, the vulnerability of urban rainstorm flood disasters was analyzed with one highly popular area of research in mind: big data. Web crawler technology was used to extract text information related to floods from Internet and popular social media platforms. Combining these text data with traditional statistical data, a flood disaster vulnerability assessment model based on Analytic Hierarchy Process (AHP) was established to evaluate rainstorm and flood disaster vulnerability, and the spatial distribution characteristics of vulnerability to pluvial flooding were analyzed based on Geographic Information System (GIS). The established model was applied in Zhengzhou, a city that often suffers from heavy rainstorms. The results show that the areas located near downtown Zhengzhou were more vulnerable to rainstorm and flooding than others, and most of the city could be at moderate and high vulnerability. Finally, the waterlogging spots extracted from various sources were used to evaluate the performance of the proposed model. The results show that most of waterlogging spots were located in very-high and high risk zones, while less waterlogging spots were found in districts with low vulnerability, which demonstrates the discriminative power of the established model based on big data sources. This study overcomes limited data in flood disaster vulnerability assessment methods and provides a basis for flood control and management in cities.

Assessing urban flood disaster risk using Bayesian network model and GIS applications

“…There are many commonly used models, such as SCS-CN (Kazak et al 2018), SWMM (Huang and Jin 2019), DRAINMOD (Youssef et al 2018), and so on. These models typically require various types of data such as rainfall data, basic geographic data and socio-economic data (Wu et al 2019). The ability of models is limited by data limitation or data-sparse, such as invalid values of model parameters, inadequate or erroneous information needed to apply the models such as input and calibration data, the defective description of relationships among data, etc.…”

Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network

“…The urban flood inundation risk is affected by many complex and multiple factors, such as rainfall, topography, river network, vegetation, soil, etc. In most previous studies, it is considered that those factors effect on the inundation risk directly [12,14,26,56]. However, those influencing factors are interdependent, affecting flood inundation indirectly in the form of disaster-causing factors chains.…”

Understanding China’s Urban Rainstorm Waterlogging and Its Potential Governance

“…Rainstorms often affect the normal operation of the whole city through the weak links in the city [27]. The existing urban drainage pipe network is old, the management is chaotic, and it has not been improved with global warming and regional environment changes, which leads to a low level of urban storm waterlogging protection compared with the increasing number of rainstorm [29,32]. At the top level of urban development, emergency planners and urban development planners do not pay enough attention to urban rainstorm waterlogging (a basic security issue), leading to the frequent occurrence of urban rainstorm waterlogging in China at an institutional level [21,23].…”

“…Considering the background of severe rainstorm waterlogging, how to better understand the natural and human causes of rainstorm waterlogging in China is not only the focus of academic circles [32][33][34][35][36][37], but also the basic work of the government (i.e., to carry out rainstorm waterlogging control). It is also one of the important aspects of this paper.…”