A Hybrid Deep Learning Model for Enhanced Greenhouse Gas Emission Forecasting in UK

Introduction

Greenhouse gas (GHG) emissions, particularly methane (CH4) and carbon dioxide (CO2), are critical contributors to global warming and climate change. Methane, though emitted in smaller quantities, has over 80 times the global warming potential of CO2 over 20 years, making it a high-impact target for climate action. Accurate forecasting of these emissions is essential for developing effective reduction strategies and achieving global climate goals.

To address this need, we developed a hybrid deep learning model that combines Long Short-Term Memory (LSTM) networks with a multi-head self-attention mechanism (MSA). This innovative approach enhances the prediction of CH4 and CO2 emissions, providing actionable insights for environmental policymakers and researchers.

Proposed Model

The proposed model, called LSTM-MSA, integrates the strengths of LSTM networks and self-attention mechanisms:

LSTM : Capture long-term dependencies and temporal patterns in GHG emissions data.
Multi-Head Self-Attention: Identifies the most relevant time steps and features, improving the model's focus and accuracy.

Our model uses historical data on CH4 and CO2 emissions, along with meteorological variables such as temperature, wind speed, humidity, and pressure. Data from the UK DECC Network, spanning hourly measurements between 2018 and 2021, was preprocessed using a sliding window approach. Each input sequence of 25 hourly measurements was used to predict GHG concentrations for the next hour.

Architecture of the proposed LSTM-MSA model for GHG prediction.

Results

The LSTM-MSA model demonstrated exceptional performance in predicting methane (CH4) and carbon dioxide (CO2) emissions. For methane, the model achieved high accuracy with a mean squared error (MSE) of 6.0, a root mean squared error (RMSE) of 0.37, and a mean absolute error (MAE) of 0.18. The coefficient of determination (R²) value of 0.85 further highlights its reliability in methane forecasting.

For carbon dioxide, the model performed equally well, achieving an MSE of 0.62, RMSE of 0.29, and MAE of 0.17, with an R² value of 0.91. These results underscore the model's ability to accurately capture the dynamics of GHG emissions, significantly outperforming traditional methods such as Support Vector Regression, Gated Recurrent Units, and standalone LSTM models.

Predicted vs Actual Methane Concentrations over Time.

Predicted vs Actual CO2 Concentrations over Time.

Additionally, SHapley Additive exPlanations (SHAP) analysis revealed the importance of meteorological factors in driving fluctuations in emissions. Variables like wind speed, temperature, and humidity were identified as key contributors, offering valuable insights for designing targeted mitigation strategies.

Conclusions

This study demonstrates the potential of hybrid deep learning models like LSTM-MSA to tackle complex environmental challenges such as GHG forecasting. Beyond CH4 and CO2, the framework can be extended to predict other greenhouse gases, such as nitrous oxide (N2O). Future work will focus on deploying this model for real-time monitoring, enabling data-driven decisions to combat climate change effectively.

By leveraging AI for enhanced emission predictions, this research contributes to the global effort toward a sustainable and greener future.

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