Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling

Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling

In this study, three novel machine learning algorithms of additive regression-random forest (AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were explored for carbon dioxide (CO2) flux rate prediction from three agricultural fields. To build the dataset, 401 samples were...

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Bibliographic Details
Main Authors: Muhammad Hassan, Khabat Khosravi, Aitazaz A. Farooque, Travis J. Esau, Alaba Boluwade, Rehan Sadiq
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Smart Agricultural Technology
Subjects:
Greenhouse gases
Soil-climatic variables
Hybrid models
Predictive analytics
Online Access:http://www.sciencedirect.com/science/article/pii/S2772375524001643
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