Abstract
Influenced by climate change and urbanization, urban flood frequently occurs and represents a serious challenge for many cities. Therefore, it is necessary to generate refined predictions of urban floods, such as the prediction of water accumulation processes at water accumulation points, which is of great significance for supporting water-related managers to reduce flood losses. In this study, 16 combination schemes of rainfall sensitivity indicators were used to determine the optimal scheme for predicting the depth of accumulated water, and the gradient boosting decision tree (GBDT) algorithm in deep learning was used to build a prediction model of the accumulation process of urban stormy accumulation points. Among the 16 schemes, the relative error of scheme 1 is 15.39%, and the qualified rate is 92.86%. This scheme exhibits the highest accuracy for the prediction results of water accumulation depth. Given this finding, the GBDT algorithm was used to construct a regression prediction model of the water accumulation process based on the collected historical rainfall water accumulation data of 50 water accumulation points. The results demonstrated that the GBDT regression prediction model has a mean relative error of 19.77%, a qualified rate of 82.00%, and a peak average relative error of 5.48%, which verify the validity and applicability of the model for the real-time prediction of the process of water accumulation.
Highlights
In recent years, global warming and urbanization have led to the increasing frequency and influence of urban floods [1], [2], posing severe challenges to urban flood control and drainage
The logistic regression model of water depth of 16 different index combination schemes is constructed by using SQL Server Data Tools, which is a data processing and analysis software of Microsoft
The Mean relative error (MRE) of water accumulation depth prediction with concentration skewness is reduced by 47.35% relative to no concentration skewness, indicating that the concentration skewness, a new rainfall characteristic index proposed in this study, has good applicability to the prediction of water accumulation depth
Summary
Global warming and urbanization have led to the increasing frequency and influence of urban floods [1], [2], posing severe challenges to urban flood control and drainage. The heavy losses caused by urban floods have made people attach great importance to urban flood prevention and control [7]. The associate editor coordinating the review of this manuscript and approving it for publication was Nilanjan Dey. large number of engineering and nonengineering measures to continuously promote urban flood prevention and control work [8]. Some engineering measures, such as improving the design standard of urban drainage pipe networks, increasing artificial lakes, and repairing deep tunnels in the city, were used to improve the flood control capacity of the city to alleviate the losses caused by urban flooding as much as possible. Urban drainage capacity and urban waterlogging prevention capacity have been improved to a certain extent, urban floods still frequently occur
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