Urban rainstorm waterlogging has become a typical “city disease” in China. It can result in a huge loss of social economy and personal property, accordingly hindering the sustainable development of a city. Impervious surface expansion, especially the irregular spatial pattern of impervious surfaces, derived from rapid urbanization processes has been proven to be one of the main influential factors behind urban waterlogging. Therefore, optimizing the spatial pattern of impervious surfaces through urban renewal is an effective channel through which to attenuate urban waterlogging risk for developed urban areas. However, the most important step for the optimization of the spatial pattern of impervious surfaces is to understand the mechanism of the impact of urbanization processes, especially the spatiotemporal pattern of impervious surfaces, on urban waterlogging. This research aims to elucidate the mechanism of urbanization’s impact on waterlogging by analysing the spatiotemporal characteristics and variance of urban waterlogging affected by urban impervious surfaces in a case study of Guangzhou in China. First, the study area was divided into runoff plots by means of the hydrologic analysis method, based on which the analysis of spatiotemporal variance was carried out. Then, due to the heterogeneity of urban impervious surface effects on waterlogging, a geographically weighted regression (GWR) model was utilized to assess the spatiotemporal variance of the impact of impervious surface expansion on urban rainstorm waterlogging during the period from the 1990s to the 2010s. The results reveal that urban rainstorm waterlogging significantly expanded in a dense and circular layer surrounding the city centre, similar to the impervious surface expansion affected by urbanization policies. Taking the urban runoff plot as the research unit, GWR has achieved a good modelling effect for urban storm waterlogging. The results show that the impervious surfaces in the runoff plots of the southeastern part of Yuexiu, the southern part of Tianhe and the western part of Haizhu, which have experienced major urban engineering construction, have the strongest correlation with urban rainstorm waterlogging. However, for different runoff plots, the impact of impervious surfaces on urban waterlogging is quite different, as there exist other influence factors in the various runoff plots, although the impervious surface is one of the main factors. This result means that urban renewal strategy to optimize the spatial pattern of impervious surfaces for urban rainstorm waterlogging prevention and control should be different for different runoff plots. The results of the GWR model analysis can provide useful information for urban renewal strategy-making.

With the rapid expansion of impervious surfaces, urban waterlogging has become a typical “urban disease” in China, seriously hindering the sustainable development of cities. Therefore, reducing the impact of impervious surfaces on surface runoff is an effective approach to alleviate urban waterlogging. Presently, the development mode of many cities in China has shifted from an increase in urban scale to the improvement of urban quality through urban renewal, which is the current and future development path for most cities. Optimizing the design of impervious surfaces in urban renewal planning to reduce its impact on surface runoff is an important way to prevent and control urban waterlogging. The aim of this research is to construct an optimization model of impervious surface space layout under the framework of a geographic simulation technology-integrated ant colony optimization (ACO) and Soil Conservation Service curve number (SCS-CN) model (ACO-SCS) in a case study of Guangzhou in China. Urban runoff plots in the study area are divided according to the area of the urban planning unit. With the goal of minimizing the runoff coefficient, the optimal space layout of the impervious surfaces is obtained, which provides a technical method and reference for urban waterlogging prevention and control through urban renewal planning. The results reveal that the optimization of impervious surface space layout through ACO-SCS achieves a satisfactory effect with an average optimization rate of 9.52%, and a maximum optimization rate of 33.16%. The research also shows that the initial impervious surface layout is the key influencing factor in ACO-SCS. In the urban renewal planning stage, the space layout of the impervious surfaces with a high–low–high density discontinuous connection can be constructed by transforming medium-density impervious surfaces into low-density impervious surfaces to achieve the flat and long-type agglomeration of the low-density and high-density impervious surfaces, which can effectively reduce the influence of urban development on surface runoff. There is spatial heterogeneity of the optimal results in different urban runoff plots. Therefore, the policy of urban renewal planning for urban waterlogging prevention and control should be different. The optimized results of impervious surface space layout provide useful reference information for urban renewal planning.

Floods occur frequently and cause considerable damage to local environments. Effectively assessing the flood risk contributes to reducing loss caused by such disasters. In this study, the weighted naïve Bayes (WNB) method was selected to evaluate flood risk, and the entropy weight method was employed to compute the weights. A sampling and verifying model was employed to generate the most accurate conditional probability table (MACPT) to calculate the probability of flooding. When using the framework integrating WNB with the sampling and verifying model, previous studies could not obtain a WNB-based MACPT and the WNB classification accuracy, for lacking WNB functions that could be called directly. Facing this issue, in this study we developed WNB functions with the MATLAB platform to directly integrate with the sampling and verifying model to generate a WNB-based MACPT, contributing to the greater interpretability and extensibility of the model. Shantou and Jieyang cities in China were selected as the study area. The results demonstrate that: (1) a WNB-based MACPT can reflect the real spatial distribution of flood risk and (2) the WNB outperform the NB when integrated with the sampling and verifying model. The resulting gridded estimation reveal a detailed spatial pattern of flood risk, which can serve as a realistic reference for decision making related to floods. Furthermore, the proposed method uses less data, which would be helpful in developing countries where long-term intensive hydrologic monitoring is limited.

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