An urban waterlogging intelligent prediction method and system based on a physically-constrained enhanced video generative adversarial network

Abstract

The application discloses an urban waterlogging intelligent prediction method and system based on a physically constrained enhanced video generative adversarial network. The method comprises the following steps: constructing a meteorological geographic data set containing static terrain data and dynamic rainfall data; generating waterlogging simulation data based on a physical numerical simulation model, and matching the waterlogging simulation data with the meteorological geographic data set into a comprehensive data set; constructing a physically constrained enhanced video generative adversarial network model, wherein the model comprises a generator and a discriminator; constructing a multi-angle loss function system that fuses an adversarial loss, a time sequence consistency loss, a data supervision loss and a water quantity balance constraint physical loss; and alternately optimizing the generator and the discriminator until the model converges, and outputting a spatiotemporally continuous waterlogging inundation depth prediction result. The application explicitly embeds a physical hydrological law into a deep learning network, and eliminates the physical logic contradiction of a pure data-driven model in continuous spatiotemporal sequence prediction through water quantity balance constraint.

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