期刊论文

Zhang, CX;Shen, DT

英文

International Journal of Applied Earth Observation and Geoinformation

1569-8432

2024

131

10.1016/j.jag.2024.103972

National Natural Science Foundation of China [42371425, 42077438, 42325107, 42330108]; Fundamental Research Funds for the Central Universities [CCNU24JC029]

本校为其他机构

Given the increased incidence of pluvial floods due to climate change and urbanization, the demand for highly efficient and accurate modeling within urban drainage systems has intensified, making machine learning and deep learning techniques increasingly popular. Nonetheless, these data-driven approaches face challenges in adequately capturing and interpreting dynamic process-evolving features, especially the spatiotemporal effects emanating from manholes during urban waterlogging events. To address these issues, this study proposes a general framework that extracts the spatiotemporal effects ...