The invention relates to the technical field of
water environment monitoring, and discloses a
lake water quality multi-parameter
deep learning inversion framework based on hyperspectral data, and the framework comprises the following steps: S1,
data preparation and preprocessing: obtaining the hyperspectral data of
lake water quality and corresponding
water quality in-situ data, and preprocessing the hyperspectral data and the
water quality in-situ data; s2, constructing a
feature extraction and parameter inversion model which sequentially comprises a one-dimensional
convolutional neural network module, a bidirectional long-short-
term memory network module and a three-dimensional attention module, and inputting the preprocessed hyperspectral data into the model. According to the method, loss distribution can be dynamically optimized according to inversion requirements of different
water quality parameters, the accuracy and generalization ability of simultaneous inversion of multiple parameters such as
chlorophyll a,
total suspended solids and transparency are remarkably improved, dependence of a traditional model on specific
water body types is broken through, and the method can adapt to
lake water bodies with different hydrological and optical characteristics.