A spatial assessment of urban waterlogging risk based on a Weighted Naive Bayes classifier

Urban waterlogging occurs frequently and often causes considerable damage that seriously affects the natural environment, human life, and the social economy. The spatial evaluation of urban waterlogging risk represents an essential analytic step that can be used to prevent urban waterlogging and minimize related losses. The Weighted Naive Bayes (WNB) classifier is a powerful method for knowledge discovery and probability inference under conditions of uncertainty; a WNB classifier can be applied to estimate the likelihood of hazards. Six spatial factors were considered to be added to the WNB, which may improve the efficiency in predicting urban waterlogging risk during analysis. As such, a spatial framework integrating WNB with GIS was developed to assess the risk of urban waterlogging using the primary urban area of Guangzhou in China as an example. The results show that 1) the rationality of six spatial factors was determined according to the Conditional Probability Tables and weights; 2) the Most Accurate Sampling Table has objectivity; and 3) the areas with a high likelihood of waterlogging risk were mainly located in the southwestern part of the study area. The northeastern zones are relatively free of waterlogging risk. The results reveal a more accurate spatial pattern of urban waterlogging risk that can be used to identify risk hot spots. The resulting gridded estimates provide a realistic reference for decision making related to urban waterlogging. (C) 2018 Elsevier B.V. All rights reserved.