Time-Series Modeling of Methane Emission Events Using Machine Learning Forecasting Algorithms

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@article{1709946,
      author = {Semiu Temidayo Fasasi, Oluwapelumi Joseph Adebowale, Abdulmaliq Abdulsalam, Zamathula Queen Sikhakhane Nwokediegwu},
      title = {Time-Series Modeling of Methane Emission Events Using Machine Learning Forecasting Algorithms},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {4},
      pages = {337-346},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709946.pdf},
      abstract = {Methane is a significant contributor to global warming, necessitating accurate monitoring and forecasting of its emissions to inform effective mitigation strategies. This paper investigates the application of machine learning algorithms for time-series modeling of methane emission events, addressing the challenges posed by the complex, non-linear, and noisy nature of environmental data. A comprehensive methodology is developed, incorporating advanced data preprocessing techniques and the evaluation of multiple forecasting models, including Long Short-Term Memory networks and ensemble methods such as Random Forest and Gradient Boosting. The comparative analysis demonstrates that these machine learning approaches outperform traditional statistical methods in capturing temporal dependencies and episodic emission spikes. Furthermore, the inclusion of contextual environmental variables enhances prediction accuracy and interpretability. The study highlights the potential of machine learning to provide reliable, actionable forecasts that support proactive environmental monitoring, regulatory compliance, and emission reduction efforts. Key challenges such as data quality, model interpretability, and computational demands are discussed, along with recommendations for future research focusing on multimodal data integration and adaptive learning frameworks. This work contributes to advancing data-driven approaches for methane emission forecasting, offering valuable insights for environmental scientists and policymakers engaged in climate change mitigation.},
      keywords = {Methane Emissions, Time-Series Forecasting, Machine Learning, Long Short-Term Memory, Environmental Monitoring, Emission Prediction},
      month = {October},
  }