Predicting Soil Textural Classes Using Random Forest Models: Learning from Imbalanced Dataset

Predicting Soil Textural Classes Using Random Forest Models: Learning from Imbalanced Dataset

Soil provides a key interface between the atmosphere and the lithosphere and plays an important role in food production, ecosystem services, and biodiversity. Recently, demands for applying machine learning (ML) methods to improve the knowledge and understanding of soil behavior have increased. Whil...

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Bibliographic Details
Main Authors: Sina Mallah, Bahareh Delsouz Khaki, Naser Davatgar, Thomas Scholten, Alireza Amirian-Chakan, Mostafa Emadi, Ruth Kerry, Amir Hosein Mosavi, Ruhollah Taghizadeh-Mehrjardi
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Agronomy
Subjects:
digital soil mapping
machine learning
soil particle size fraction
imbalance classification
data resampling
Iran
Online Access:https://www.mdpi.com/2073-4395/12/11/2613

Internet

https://www.mdpi.com/2073-4395/12/11/2613

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