ICCK

Jonas Nnaemeka Onah

Federal University of Petroleum Resources Effurun, Delta State, Nigeria

Section 01

Academic Profile

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Section 02

Editorial Roles

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Section 03

ICCK Publications

Free Access | Research Article | 12 January 2026 | Cited: Crossref logo  2 , Scopus 1
A Data-Driven Framework for Methane Emission Prediction Using Machine Learning Methods
ICCK Transactions on Machine Intelligence | Volume 2, Issue 1: 53-64, 2026 | DOI: 10.62762/TMI.2025.782852
Abstract
Greenhouse gas Methane (CH$_4$) has 86 times more impact on global warming than carbon dioxide (CO$_2$). The emission of methane gas into the atmosphere is increasing due to the reliance on fossil-based resources in post-industrial energy consumption, along with the rise in food demand and the generation of organic waste that accompanies a growing human population. CH$_4$ acts as a vital pollutant in the air. The problem addressed in this study was to accurately estimate CH$_4$ emissions from functional urban areas. This study aims to predict CH$_4$ emissions using Time Series (TS) and Machine Learning (ML) models such as Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SAR... More >

Graphical Abstract
A Data-Driven Framework for Methane Emission Prediction Using Machine Learning Methods