Directed Graph Neural Network for High-Temperature Forecasting
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Solution Overview
Problem
Traditional meteorological forecast methods, particularly for high-temperature disasters, face challenges in accurately simulating the interactions among meteorological factors and handling long time series due to limitations in feature extraction and model complexity, leading to poor performance in capturing nonlinear relationships and overfitting.
Innovation Solution
A directed graph neural network (DGNN) is employed to model high-temperature disaster forecasts by standardizing meteorological elements, constructing a multidimensional time series sample set, and training a DGNN model that aggregates node information through a graph learning strategy, incorporating an attention mechanism and temporal convolutional module to capture time periodic modes and interactions among variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional statistical models are used to forecast high-temperature disasters, then the model structure is simple and easy to implement, but the model cannot accurately simulate the nonlinear interactions among meteorological factors and performs poorly in capturing complex change processes
Solution Approach 1:
The patent replaces traditional statistical modeling methods with a deep learning-based directed graph neural network. This substitution enables the system to automatically learn complex nonlinear interactions among meteorological factors without relying on predefined mechanical model structures, thereby significantly improving forecast accuracy while maintaining reasonable system complexity.
Solution Approach 2:
The patent combines multiple advanced techniques into a composite forecasting system: directed graph neural networks for capturing variable interactions, attention mechanisms for focusing on critical factors, and temporal convolutional modules for handling time series data. This composite approach synergistically addresses the limitations of individual methods in capturing complex meteorological interactions.
2Measurement precision
If traditional deep learning networks are used to process meteorological time series data, then the model can capture nonlinear relationships, but the model complexity increases quadratically with data amount, leading to overfitting and poor performance on long time series
Solution Approach 1:
The patent segments the processing of meteorological time series data by introducing a directed graph structure that divides variables into nodes and their interactions into edges. This segmentation allows the model to process high-dimensional data in a structured manner, avoiding quadratic complexity growth while maintaining the ability to capture nonlinear relationships through localized neighborhood aggregations.
Solution Approach 2:
The patent transforms the traditional flat data processing approach into a graph-structured representation, adding a structural dimension to the data. By representing meteorological variables as nodes in a directed graph and their interactions as edges, the model efficiently captures complex relationships without quadratic complexity increase, as graph neural networks scale linearly with the number of nodes and edges rather than quadratically.
3Adaptability or versatility
If traditional deep learning networks are applied to multivariate meteorological data, then the model can process multiple variables, but it is difficult to simulate how changes in one variable interact with other variables and the whole environmental state
Solution Approach 1:
The patent introduces a directed graph structure as an intermediary representation between raw multivariate meteorological data and the deep learning model. This graph structure explicitly encodes the interaction relationships among variables, serving as a mediator that preserves interaction information while enabling efficient processing. The attention mechanism further acts as an intermediary to selectively focus on the most relevant variable interactions.
Solution Approach 2:
The directed graph neural network framework provides a universal representation that can handle any number of meteorological variables and their interactions. The graph structure naturally accommodates multivariate data by representing each variable as a node and their relationships as edges, allowing the model to universally process complex multivariate interactions without losing information about how changes in one variable affect others.
Data Source
AI summary
A high-temperature disaster forecast method based on a directed graph neural network is provided, and the method includes the following steps: S1, performing standardization processing on meteorological elements respectively to scale the meteorological elements into a same value range; S2, taking the meteorological elements as nodes in the graph, and describing relationships among the nodes by an adjacency matrix of graph; then learning node information by a stepwise learning strategy and continuously updating a state of the adjacency matrix; S3, training the directed graph neural network model after determining a loss function, obtaining a model satisfying requirements by adjusting a learning rate, an optimizer and regularization parameters as a forecast model, and saving the forecast model; and S4, inputting historical multivariable time series into the forecast model, changing an output stride according to demands, and thereby obtaining high-temperature disaster forecast for a future period of time.