Method for GNN-BASED Automatic Bay Zoning

A method for GNN-based automatic bay zoning, comprising 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;
• the data packet in the S1 is trim2nc, which was originally compiled and run based on matlab software, but now is run through the Python software after modification, and data conversion codes for grid positions, flow velocities, temperatures, and densities are added;
• and the output file of the hydrodynamic model is in a .dat format, and the hydrodynamic model is one of a Delft3D model and a MIKE model;
• S2, reading the converted .nc file and performing data preprocessing;
• a specific process of the S2 comprises:
• S21, data reading:
• using the Python software to read the converted .nc file, and obtaining data of each grid of the bay comprising a temperature, a salinity, a friction coefficient, a flow velocity, a topography and a density;
• S22, time series data filling:
• expanding the data comprising the temperature, the salinity, the friction coefficient, the flow velocity, the topography and the density into time series data respectively, and matching cross-sectional data with remaining time series data, wherein the friction coefficient and the topography belong to the cross-sectional data, and the temperature, the salinity, the flow velocity and the density belong to the remaining time series data;
• S23, land-sea identification:
• constructing a land-sea mask matrix according to grid data output by the hydrodynamic model, wherein in the mask matrix, a land grid is encoded as 0 and a sea grid is encoded as 1;
• S24, mask extraction:
• based on results of the land-sea identification, performing the mask extraction of a bay zone through the land-sea mask matrix;
• and
• S25, neighboring interpolation filling:
• filling a missing value of the flow velocity after averaging through a surrounding 3×3 grid;
• S3, using a tsfresh data package to extract time series feature values of different features;
• in the S3, the tsfresh data packet in the Python software is used to extract all time series eigenvalues in batches, and the time series eigenvalues include absolute energy, an absolute value of continuous change, unit root test results, and a centroid of a Fourier transform spectrum;
• 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;
• the convolutional GNN in S5 is constructed using an existing graph convolutional network (GCN) framework in a pytorch software data packet, wherein a loss function is calculated by distinguishing positive and negative samples and summing structural losses thereof, with a specific calculation formula as follows:
• positive_loss
• =
• ∑
• (
• i
• ,
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• )
• ∈
• positive_pairs
• ⁢
• 
• embeddings
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• embeddings
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• [
• j
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• 2
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• "\[LeftBracketingBar]"
• positive_pairs
• ❘
• "\[RightBracketingBar]"
• negative_loss
• =
• ∑
• (
• i
• ,
• j
• )
• ∈
• negative_pairs
• ⁢
• max
• ⁢
• (
• 0
• ,
• margin
• -
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• embeddings
• [
• i
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• -
• embeddings
• [
• j
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• 2
• ❘
• "\[LeftBracketingBar]"
• negative_pairs
• ❘
• "\[RightBracketingBar]"
• total_loss
• =
• positive_loss
• +
• negative_loss
• in the formula, positive_loss represents a positive sample loss;
• (i, j)_represents a grid located at an ith row and a jth column;
• embeddings represents a feature embedding matrix of nodes;
• embeddings[i] represents an embedding vector of an ith node;
• embeddings[j] represents an embedding vector of a jth node;
• positive_pairs represents a set of all positive sample pairs in a graph;
• negative_loss represents a negative sample loss;
• negative pairs represents a set of all negative sample pairs in the graph;
• margin is a hyperparameter used to define an interval between negative sample pairs;
• and total_loss represents a total loss;
• S6, using a Louvain algorithm or a Spectral Clustering algorithm to perform unsupervised classification of bay;
• S7, post-processing broken edges of bay zoning through a neighboring grid consistency method;
• and
• S8, based on the convolutional GNN, outputting zoning results in the form of a .shp file to realize the automatic bay zoning.