Graph Neural Network Air Pollutant Forecasting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current air pollutant concentration forecasting methods lack the ability to effectively integrate and utilize the temporal and spatial dynamic characteristics of air pollutants, resulting in weak generalization capability and low forecast accuracy.
Innovation Solution
A method utilizing a graph neural network-based approach that constructs a training set, validation set, and test set from pollutant concentration and meteorological data, building a neural network model that incorporates the spatial distribution of monitoring stations as a graph structure, allowing for improved prediction accuracy and generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional theoretical-based or statistical-based methods are used for air pollutant concentration forecasting, then the forecasting can be performed with existing data, but the generalization ability and forecast accuracy are weak due to inability to integrate temporal and spatial dynamic characteristics
Solution Approach 1:
The patent transforms the traditional flat data structure into a graph-structured representation that incorporates spatial dimensions. By constructing an adjacent matrix based on spatial distribution of monitoring stations and using graph neural networks, the model adds spatial dimensionality to the forecasting approach, enabling simultaneous capture of temporal sequences and spatial relationships, thereby resolving the contradiction between forecast accuracy and generalization ability
Solution Approach 2:
The patent creates a composite forecasting model that integrates multiple data types (pollutant concentration data and meteorological data) and multiple spatial relationships (adjacent matrix constructed from spatial distribution). This composite approach combines heterogeneous information sources and spatial-temporal features into a unified graph neural network framework, improving both accuracy and generalization capability
2Quantity of substance
If monitoring stations are sparsely distributed in China, then the data acquisition cost is reduced, but the ability to provide effective and accurate data for analysis and research deteriorates
Solution Approach 1:
The patent creates a virtual copy of the sparse monitoring network through graph neural network modeling. By constructing an adjacent matrix that represents spatial relationships and using the model to infer pollutant concentrations at unmonitored locations, the system effectively replicates the monitoring capability across the entire region without physically deploying additional stations, thus maintaining data quality while keeping the number of stations low
Solution Approach 2:
The graph neural network model acts as an intermediary that bridges the gap between sparse monitoring stations and the need for comprehensive spatial coverage. The model uses the adjacent matrix as a mediator to propagate and interpolate spatial information, enabling accurate forecasting across regions with limited monitoring infrastructure
Data Source
AI summary
A method, apparatus and storage medium for forecasting air pollutant concentration, including: constructing a training set, a validation set and a test set based on a data set; the data set is obtained by collecting pollutant concentration data and meteorological data in a predetermined length of time in a target area; constructing an adjacent matrix A of a graph structure based on the spatial distribution of monitoring stations in the target area; establishing a neural network model F(x;Θ|A), where x is the input data of it, including pollutant concentration data and meteorological data within predetermined time period, training the neural network model using the data of the training set, adjusting the parameters Θ of the neural network model using the data of the validation set and the data of the test set, and obtaining the modified neural network model; using the modified neural network model for air pollutant concentration forecasting.