Abstract:

Urban waterlogging is one of the most common natural disasters. In-depth analysis of its influencing factors and estimation of high-risk waterlogging areas are of great significance for waterlogging prevention and management. Although some studies have approached these issues through advanced machine learning methods such as random forest and neural network, the identified influencing factors are mainly related to the two-dimensional space. Moreover, while traditional methods require both accurate positive and negative samples, there is an inevitable subjectivity in the selection of negative samples. To address these disadvantages, this research took Shenzhen City as the study area and employed the MAXENT model, which does not require negative samples, to explore the relationship between potential influencing factors (including three-dimensional building factors) and waterlogging risk during 2015-2019. The results show that the dominant environmental factors behind the density of waterlogging hotspots were the proportion of impervious surface, the proportion of green space, population density, rainstorm peak rainfall, and fluctuation of the terrain. With regard to the three-dimensional building factors, building congestion, average building height, and building shape coefficient have a crucial impact on urban waterlogging. According to the waterlogging probability estimated by MAXENT, the total area of potential high-risk waterlogging areas in Shenzhen is approximately 491 km², accounting for 24.58% of the total area of the city. These areas are mainly located in Longhua District, Nanshan District, the north of Longgang District, Guangming District, and Futian District. Through the spatial autocorrelation analysis of the potential high-risk areas, we found that some areas in the north of Nanshan District, the west of Futian District, and central Luohu District where there were no waterlogging hotspots in the past, exhibit high concentration levels. This indicates that the waterlogging probability in these areas would be positively affected by the surrounding areas. Therefore, focus should be placed on high-risk areas for achieving more accurate waterlogging prevention and management. Urban waterlogging risk assessment is an important part of disaster management. The assessment results of waterlogging risk not only can provide support for disaster prevention and risk mitigation, but also are essential for protecting people's lives and the sustainable development of cities.

Key words: urban waterlogging, MAXENT, three-dimensional building configuration, random forest, risk assessment, Shenzhen City