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<p>Methane, a potent greenhouse gas with a global warming potential significantly higher than carbon dioxide, plays a critical role in climate change. Accurate predictions of its future concentrations are vital for understanding and mitigating its environmental impact. For this reason, this paper presents a comparative analysis of deep learning models—Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), CNN (Convolutional Neural Network)-GRU, and CNN-LSTM—for forecasting atmospheric methane concentrations through 2050. Leveraging historical data, each model’s performance was evaluated using key metrics, including Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency (NSE). The results reveal that the CNNLSTM model achieved the highest accuracy, with the lowest MAE of 0.6567 and the highest NSE score of 0.933, indicating its superior capability in capturing the complexities of methane concentration trends. In contrast, the GRU model exhibited the poorest performance, with an MAE of 0.9667 and an NSE score of 0.844. Projections for 2050 indicate significant increases in methane levels, with maximum yearly concentrations expected to reach up to 2199.76 ppb, particularly under the CNN-LSTM model. These findings underscore the potential risks associated with rising methane concentrations, which could exacerbate global warming and its associated impacts. The study highlights the importance of employing advanced predictive models like CNN-LSTM to inform and enhance global climate change mitigation strategies.</p>

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Hybrid deep learning models for predicting atmospheric methane concentrations: A comparative analysis through 2050

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  • 出版模式：

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  • 发表日期：

    2025年5月30日

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  • Ihsan Uluocak

摘要

Methane, a potent greenhouse gas with a global warming potential significantly higher than carbon dioxide, plays a critical role in climate change. Accurate predictions of its future concentrations are vital for understanding and mitigating its environmental impact. For this reason, this paper presents a comparative analysis of deep learning models—Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), CNN (Convolutional Neural Network)-GRU, and CNN-LSTM—for forecasting atmospheric methane concentrations through 2050. Leveraging historical data, each model’s performance was evaluated using key metrics, including Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency (NSE). The results reveal that the CNNLSTM model achieved the highest accuracy, with the lowest MAE of 0.6567 and the highest NSE score of 0.933, indicating its superior capability in capturing the complexities of methane concentration trends. In contrast, the GRU model exhibited the poorest performance, with an MAE of 0.9667 and an NSE score of 0.844. Projections for 2050 indicate significant increases in methane levels, with maximum yearly concentrations expected to reach up to 2199.76 ppb, particularly under the CNN-LSTM model. These findings underscore the potential risks associated with rising methane concentrations, which could exacerbate global warming and its associated impacts. The study highlights the importance of employing advanced predictive models like CNN-LSTM to inform and enhance global climate change mitigation strategies.