Environmental Science & Deep Learning
Deep learning-based AQI forecasting: a CNN-LSTM model with visual insights from SHAP-LIME and PDP
This research introduces a hybrid CNN-LSTM model for multi-step Air Quality Index (AQI) forecasting, outperforming traditional and isolated deep learning models. It achieved robust predictive capability, especially for Anugul city, with a Mean Squared Error (MSE) of 130.66, Root Mean Squared Error (RMSE) of 11.40, and Mean Absolute Error (MAE) of 8.38. The integration of SHAP, LIME, and PDP provides critical interpretability, revealing PM2.5 and PM10 as dominant pollutants. The model offers stable AQI prediction and supports data-driven environmental policymaking.
Executive Impact & Key Findings
Implementing this advanced AQI forecasting system can lead to substantial operational efficiencies, improved public health outcomes, and more targeted environmental policies. For an organization managing air quality, this translates to reduced response times to pollution events, optimized resource allocation, and averted regulatory fines.
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Avg. MAE (Anugul)
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Prediction Accuracy
Deep Analysis & Enterprise Applications
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8.38
Lowest MAE (Anugul, Adam Optimizer, 200 Epochs)
The Adam optimizer at 200 epochs yielded the minimum Mean Absolute Error (MAE) of 8.38 for Anugul data, demonstrating superior predictive accuracy compared to RMSprop and SGD. This metric highlights the model's ability to provide precise and reliable AQI predictions in industrial areas, minimizing prediction errors.
CNN-LSTM AQI Prediction Workflow
Collection of Data
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Time series data construction
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Segmentation of data set
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Input training set and validation set
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Normalization of data
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Model training
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Input test set
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Data normalization
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Prediction
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Comparison
The proposed CNN-LSTM model integrates Convolutional Neural Networks (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for capturing long-term temporal dependencies. This hybrid architecture, detailed in the workflow, processes multivariate time series data to forecast AQI with high accuracy and interpretability.
Model Performance & Interpretability
The CNN-LSTM model, optimized with Adam, demonstrates superior performance across all metrics (MSE, RMSE, MAE) compared to conventional statistical models (ARIMA, SARIMA) and isolated deep learning models. Its explainability features, SHAP, LIME, and PDP, provide crucial insights into pollutant contributions, enhancing decision-making for environmental policy.
| Metric | CNN-LSTM with Adam | Other Models |
|---|---|---|
| Predictive Accuracy | Highly Robust (MAE: 8.38) | Lower (ARIMA MAE > 30) |
| Interpretability | High (SHAP, LIME, PDP insights) | Limited/Opaque |
| Temporal & Spatial Learning | Excellent (Hybrid model) | Limited (Isolated models) |
| Adaptability | Flexible (Small-to-medium datasets) | Requires large datasets |
Targeted Air Quality Management in Anugul, Odisha
The study's focus on Anugul, an industrially dominated non-megacity, provides unique insights. PM10 and PM2.5 were identified as the primary drivers of AQI fluctuations through SHAP and PDP analysis. Seasonal trends showed winter had the highest AQI, necessitating season-specific mitigation. This localized approach allows for more effective, evidence-based interventions than generalized national policies.
Outcome: Improved insights into industry-induced pollution trends and targeted policy recommendations.
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Your AI Implementation Roadmap
A phased approach to integrating advanced AI solutions into your enterprise, ensuring smooth transition and maximum impact.
Phase 01: Strategic Planning & Data Audit
Initial consultation to define objectives, assess existing data infrastructure, and identify key integration points for the AQI forecasting system.
Phase 02: Model Customization & Training
Tailoring the CNN-LSTM model to your specific regional data (e.g., Anugul, Odisha, or India-level) and environmental variables. This includes hyperparameter tuning and validation.
Phase 03: Interpretability Integration & Validation
Implementing SHAP, LIME, and PDP for robust model explainability, allowing clear understanding of pollutant contributions and ensuring compliance.
Phase 04: Deployment & Continuous Monitoring
Seamless integration of the predictive model into your existing environmental monitoring systems, with ongoing performance evaluation and recalibration.
Phase 05: Policy Action & Impact Measurement
Leveraging precise AQI forecasts and explainable insights to inform policy-making, implement timely interventions, and measure environmental health improvements.
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