Fusion Air Quality Prediction Model for Seasonal Accuracy
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Solution Overview
Problem
Existing AQI prediction methods using machine learning models suffer from low accuracy, limited generalization, and are prone to random factors, failing to effectively consider temporal and spatial features, making them unsuitable for long-term and multi-region predictions.
Innovation Solution
A differential fusion seasonal prediction model (DF-SPM) combining a random forest model and a convolutional neural network (CNN)-long short-term memory (LSTM)-attention (CLA) model, which preprocesses historical air quality data, determines an optimal threshold interval for each season, and uses pollutant indicators to predict AQI, enhancing prediction accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single machine learning model is used for AQI prediction, then the model structure is simple, but the prediction accuracy is low and the model is vulnerable to random factors
Solution Approach 1:
The patent combines multiple machine learning models (Random Forest, CNN-LSTM-Attention) into a fusion prediction system. Each model captures different aspects of air quality patterns, and their predictions are integrated through a weighting mechanism to produce the final AQI forecast, thereby improving accuracy while managing complexity through modular architecture
Solution Approach 2:
The prediction system uses a composite approach by integrating diverse model types (tree-based Random Forest, deep sequence modeling CNN-LSTM-Attention) rather than relying on a single model. This composite structure leverages the strengths of each model type to compensate for individual weaknesses, reducing vulnerability to random factors and improving overall prediction reliability
2Device complexity
If existing prediction methods only consider temporal dependencies, then the model structure is simple, but the spatial features of AQI sequence are not considered, leading to weak generalization ability
Solution Approach 1:
The patent extends the prediction model from one-dimensional temporal sequences to two-dimensional spatiotemporal data by incorporating spatial coordinates of monitoring stations. The CNN-LSTM-Attention model processes both spatial relationships between different locations and temporal evolution, enabling the system to capture spatial patterns and improve generalization across different regions
Solution Approach 2:
The model is designed to handle multiple functions simultaneously: temporal prediction, spatial pattern recognition, and regional generalization. By integrating spatial features into the architecture, the same model can be applied to different geographic regions and air quality scenarios, enhancing its universality and adaptability
3Productivity
If the network model has limited layers, then the training process is fast, but the effectiveness of feature extraction and weight allocation is poor, limiting prediction accuracy improvement
Solution Approach 1:
The patent implements dynamic depth adjustment in the CNN-LSTM-Attention model, where the network depth and complexity are adaptively modified based on the characteristics of the input data and prediction requirements. This allows the model to increase computational depth for complex patterns while maintaining faster training for simpler cases, balancing training efficiency with prediction accuracy
Solution Approach 2:
The deep network is divided into modular segments (CNN feature extraction layer, LSTM sequence modeling layer, Attention mechanism layer) that can be independently trained and optimized. This segmentation allows for efficient training by focusing computational resources on specific functional components while maintaining the overall depth needed for accurate feature extraction and weight allocation
Data Source
AI summary
A method for predicting an air quality index (AQI) based on a fusion model proposes a differential fusion seasonal prediction model (DF-SPM) based on a random forest (RF) model and a convolutional neural network (CNN)-long short-term memory (LSTM)-attention (CLA) model. This method uses the optimal threshold interval (OTI) search algorithm to search and learn the OTI of four seasons during the prediction process, and obtains the final prediction results according to the predicted values of RF model and CLA model. The fusion model combines the prediction advantages of two independent models, and fully considers the seasonal and periodic characteristics of AQI, so as to accurately search OTI in different time periods with the seasonal scale, so as to achieve higher prediction accuracy. The OTI strategy of fusion model is superior to the single threshold strategy, which can extract the historical fluctuation characteristics of AQI and achieve higher prediction accuracy.