Abstract: A method for GNN-based automatic bay zoning includes the following steps: S1, using a modified data packet to convert an output file of the hydrodynamic model into a .nc file that can be read by Python software; S2, reading the converted .nc file and performing data preprocessing; S3, using a tsfresh data packet to extract time series feature values of different features; S4, using a Delaunay triangulation algorithm and a four-way matrix or an eight-way matrix to determine spatial connectivity, and constructing an adjacency matrix; S5, constructing a convolutional GNN to learn bay characteristics and spatial connectivity; S6, using a Louvain algorithm or a Spectral Clustering algorithm to perform unsupervised classification of bay; S7, post-processing broken edges of bay zoning; and S8, outputting zoning results in the form of a .shp file to realize the automatic bay zoning.

Abstract: A method for early warning of algal bloom levels based on an Ordinal Forests model includes the following steps: S1, preprocessing water quality data from a system for online monitoring of water quality and water ecology; S2, determining an algal bloom level according to a chlorophyll a value of the pre-processed water quality data; S3, using a resampling method to solve the problem of imbalanced algal bloom level data, and synthesizing a dataset of balanced algal bloom levels; and S4, taking the newly synthesized dataset in the S3 as an input variable, constructing a model for early warning of algal bloom levels based on the Ordinal Forests model, and performing early warning of algal bloom levels through the trained model for early warning of algal bloom levels.

Abstract: An intelligent fusion and processing method for remote sensing products for water quality monitoring of estuaries and bays includes: S1. setting criteria for image retrieval, performing iterative image retrieval and downloading, and achieving batch downloading of remote sensing images automatically; S2. performing recursive call of an Acolite settings file in Python to directly perform batch atmospheric correction of multi-source remote sensing satellite data; S3. fusing multi-source remote sensing data products based on machine learning to generate a layer of high-frequency water quality parameter remote sensing products; S4. superimposing a layer of tidal boundary vectors for different water levels onto the layer of water quality parameter remote sensing products, and adding map elements to generate a thematic map of different water quality parameters; S5. performing intelligent statistical analysis of water quality parameters; and S6.

Abstract: A method for early warning of algal bloom levels based on an Ordinal Forests model includes the following steps: S1, preprocessing water quality data from a system for online monitoring of water quality and water ecology; S2, determining an algal bloom level according to a chlorophyll a value of the pre-processed water quality data; S3, using a resampling method to solve the problem of imbalanced algal bloom level data, and synthesizing a dataset of balanced algal bloom levels; and S4, taking the newly synthesized dataset in the S3 as an input variable, constructing a model for early warning of algal bloom levels based on the Ordinal Forests model, and performing early warning of algal bloom levels through the trained model for early warning of algal bloom levels.