The invention relates to the technical field of
environmental monitoring and
atmospheric pollution early warning, in particular to a Yangtze
River Delta composite
extreme weather ozone pollution early warning model construction method, which comprises the following steps of S1, acquiring high-resolution meteorological data and
pollutant concentration data of a Yangtze
River Delta region to form an
original data set; and S2, carrying out missing value interpolation and abnormal value
elimination on the meteorological data and the
pollutant data, and carrying out grid alignment according to time and space to generate a unified spatial-temporal
characteristic matrix. According to the method, by collecting Yangtze
Delta high-resolution weather and
pollutant data, performing data cleaning, bimodal
feature coding and joint representation modeling, predicting the
ozone concentration and generating regional early warning through multi-layer Transform self-adaptive attention, the problems that traditional
ozone early warning mostly depends on a single-
modal prediction model, and the reliability of the ozone early warning is greatly improved are solved. And due to the lack of multi-
modal space-time dependent capture, the problem of early warning information
lag is caused.