Urban waterlogging prediction and risk analysis based on rainfall time series features: A case study of Shenzhen

Abstract

In recent years, the frequency of extreme weather has increased, and urban waterlogging caused by sudden rainfall has occurred from time to time. With the development of urbanization, a large amount of land has been developed and the proportion of impervious area has increased, intensifying the risk of urban waterlogging. How to use the available meteorological data for accurate prediction and early warning of waterlogging hazards has become a key issue in the field of disaster prevention and risk assessment. In this paper, based on historical meteorological data, we combine domain knowledge and model parameters to experimentally extract rainfall time series related features for future waterlogging depth prediction. A novel waterlogging depth prediction model that applies only rainfall data as input is proposed by machine learning algorithms. By analyzing a large amount of historical flooding monitoring data, a “rainfall-waterlogging amplification factor” based on the geographical features of monitoring stations is constructed to quantify the mapping relationship between rainfall and waterlogging depths at different locations. After the model is trained and corrected by the measured data, the prediction error for short-time rainfall basically reaches within 2 cm. This method improves prediction performance by a factor of 2.5–3 over featureless time series methods. It effectively overcomes the limitations of small coverage of monitoring stations and insufficient historical waterlogging data, and can achieve more accurate short-term waterlogging prediction. At the same time, it can provide reference suggestions for the government to conduct waterlogging risk analysis and add new sensor stations by counting the amplification factor of other locations.

Extracting Spatiotemporal Flood Information from News Texts Using Machine Learning for a National Dataset in China

“…In recent years, Zhengzhou had torrential rainfall and subsequent flooding on 21 July 2021, resulting in 380 deaths and direct economic losses of 168 billion dollars (Dong et al, 2022). In another example, Shenzhen experienced short-duration and extremely heavy precipitation on 11 April 2019, leading to floods and 11 deaths (Zhang et al, 2023c). Thus, urban flooding is an important risk factor affecting urban property and public safety in China.…”

Analyzing the Mitigation Effect of Urban River Channel Flood Diversion on Waterlogging Disasters Based on Deep Learning

“…Constructing an urban waterlogging warning model is an effective measure for reducing losses from waterlogging disasters. Against the background of increasing impermeable surfaces in urban areas, a warning model is required to achieve both rapid prediction speed and good prediction accuracy [23][24][25]. Given the concentration of populations in urban areas (such as the population on both sides of the Jinshui River in the study area), untimely or inaccurate warnings of waterlogging disasters may lead to severe property damage and casualties.…”

Characterization and process understanding of tropical cyclone-induced floods derived from observations in Shenzhen, China

“…This widely adopted assessment has informed the creation of a comprehensive table, enabled the estimation of specific rainfall thresholds at the subdistrict scale, and facilitated the evaluation of flood probabilities (Risk Warning for Major Meteorological Hazards in Shenzhen 2022). Machine learning (ML) has recently advanced urban flood forecasting, with studies like Zhang et al (2023), Ke et al (2020) applying ML algorithms to develop models for Shenzhen. However, these studies predominantly relied on rainfall data as the only input variable, potentially ignoring the complexity of TCs-induced floods.…”