Nitrous oxide prediction through machine learning and field-based experimentation: A novel strategy for data-driven insights

Nitrous oxide prediction through machine learning and field-based experimentation: A novel strategy for data-driven insights

Applying machine learning to predict complex environmental phenomena like greenhouse gas emissions (GHG) is gaining significant attention. This study introduces innovative ensemble learning models that integrate the randomizable filter classifier (RFC), regression by discretization (RBD), and attrib...

Full description

Saved in:
Bibliographic Details
Main Authors: Muhammad Hassan, Khabat Khosravi, Travis J. Esau, Gurjit S. Randhawa, Aitazaz A. Farooque, Seyyed Ebrahim Hashemi Garmdareh, Yulin Hu, Nauman Yaqoob, Asad T. Jappa
Format: Article
Language:English
Published: Elsevier 2025-04-01
Series:Atmospheric Environment: X
Subjects:
Greenhouse gas
N2O
Machine learning
Atlantic Canada
Online Access:http://www.sciencedirect.com/science/article/pii/S2590162125000255
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S2590162125000255

Similar Items