Digital mapping of soil phosphorous sorption parameters (PSPs) using environmental variables and machine learning algorithms

Digital mapping of soil phosphorous sorption parameters (PSPs) using environmental variables and machine learning algorithms

In this study some soil phosphorous sorption parameters (PSPs) by using different machine learning models (Cubist (Cu), random forest (RF), support vector machines (SVM) and Gaussian process regression (GPR)) were predicted. The results showed that using the topographic attributes as the sole auxili...

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Bibliographic Details
Main Authors: Sanaz Saidi, Shamsollah Ayoubi, Mehran Shirvani, Kamran Azizi, Shuai Zhao
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:International Journal of Digital Earth
Subjects:
soil fertility
random forest
adsorption isotherms
remote sensing
variable importance analysis
Online Access:http://dx.doi.org/10.1080/17538947.2023.2210314

Internet

http://dx.doi.org/10.1080/17538947.2023.2210314

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