Version 1
: Received: 6 June 2024 / Approved: 12 June 2024 / Online: 12 June 2024 (17:41:14 CEST)
How to cite:
Louime, C. J.; Raza, T. A. Development of Artificial Intelligence/Machine Learning (AI/ML) Models for Methane Emissions Forecasting in Seaweed. Preprints2024, 2024060825. https://doi.org/10.20944/preprints202406.0825.v1
Louime, C. J.; Raza, T. A. Development of Artificial Intelligence/Machine Learning (AI/ML) Models for Methane Emissions Forecasting in Seaweed. Preprints 2024, 2024060825. https://doi.org/10.20944/preprints202406.0825.v1
Louime, C. J.; Raza, T. A. Development of Artificial Intelligence/Machine Learning (AI/ML) Models for Methane Emissions Forecasting in Seaweed. Preprints2024, 2024060825. https://doi.org/10.20944/preprints202406.0825.v1
APA Style
Louime, C. J., & Raza, T. A. (2024). Development of Artificial Intelligence/Machine Learning (AI/ML) Models for Methane Emissions Forecasting in Seaweed. Preprints. https://doi.org/10.20944/preprints202406.0825.v1
Chicago/Turabian Style
Louime, C. J. and Tariq Asleem Raza. 2024 "Development of Artificial Intelligence/Machine Learning (AI/ML) Models for Methane Emissions Forecasting in Seaweed" Preprints. https://doi.org/10.20944/preprints202406.0825.v1
Abstract
This research project aimed to address the growing concern about methane emissions from seaweed by developing a Convolutional Neural Network (CNN) model capable of accurately predicting these emissions. The study used PANDAS to read and analyze the dataset, incorporating statistical measures like mean, median, and standard deviation to understand the dataset. The CNN model was trained using the ReLU activation function and mean absolute error as the loss function. Model performance was evaluated through MAPE graphs, comparing the mean absolute percentage error (MAPE) between training and validation sets and between true and predicted emissions, and analyzing trends in yearly greenhouse gas emissions. The results demonstrated that the CNN model achieved a high level of accuracy in predicting methane emissions, with a low MAPE between the expected and actual values. This approach should enhance our understanding of methane emissions from Sargassum, contributing to more accurate environmental impact assessments and effective mitigation strategies.
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
