A multi-head self-attention LSTM model for UK methane prediction

Accurately predicting methane (CH4) emissions is crucial for governments to implement appropriate policies and plans. This paper presents a multivariate approach to forecast CH4 concentrations using historical meteorological data and CH4 measurements. We introduce a hybrid deep learning model that integrates Long Short-Term Memory (LSTM) networks with multi-head self-attention mechanisms to enhance the prediction accuracy of CH4 emissions. The proposed model leverages the strengths of LSTM in capturing temporal dependencies and the self-attention mechanism’s ability to focus on relevant features across different time steps. The performance of the model is evaluated against traditional and state-of-the-art methods, demonstrating its superiority in forecasting accuracy. Our results indicate that this hybrid approach provides more reliable predictions, thereby offering valuable insights for policymakers to make informed decisions for climate change mitigation and environmental sustainability.