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Time Series Forecasting of Methane Emissions from Livestock using Machine Learning

Patole, Shambhu Chandrakant (2021) Time Series Forecasting of Methane Emissions from Livestock using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

Methane (CH4) is the infamous and strong greenhouse gas (GHG) in the environment. CH4 is almost 84 times stronger than carbon dioxide (CO2) and it has a global warming potential (GWP) of 25 over a 100-year time span. Methane has fewer concentrations and it stays in the atmosphere for almost 10 years. This study performed a comparative approach to forecast methane emissions from livestock (enteric fermentation), where the annual data from 1961-2019 was gathered from the Food and Agriculture of United Nations official website and it used three famous time-series models, ARIMA (Auto Regressive Integrated Moving Average), SVM (Support Vector Machine), and PROPHET with the goal to find the best model between these time series analysis algorithms. ARIMA performed the best among three models used that were evaluated using performance metrics that gave RMSE0.03, MAPE0.02, and MAE 0.05. Using this best model, methane emissions from livestock were also forecasted from 2020 to 2024. The results obtained in this study can help officials to concentrate more on methane emissions and bring new or change existing policies to mitigate climate change.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Agriculture Industry
G Geography. Anthropology. Recreation > GE Environmental Sciences > Environment
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Clara Chan
Date Deposited: 14 Dec 2021 10:21
Last Modified: 14 Dec 2021 10:21
URI: http://norma.ncirl.ie/id/eprint/5210

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