Comparison of Different Machine Learning Methods for Predicting Cation Exchange Capacity Using Environmental and Remote Sensing Data

Comparison of Different Machine Learning Methods for Predicting Cation Exchange Capacity Using Environmental and Remote Sensing Data

This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomorphology) to predict CEC by using different machine learning models ((random forest (RF), k-nearest neighbors (kNNs), Cubis...

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Bibliographic Details
Main Authors: Sanaz Saidi, Shamsollah Ayoubi, Mehran Shirvani, Kamran Azizi, Mojtaba Zeraatpisheh
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Sensors
Subjects:
clay type
mineralogy
remote sensing indices
valley depth
soil modeling
machine learning
Online Access:https://www.mdpi.com/1424-8220/22/18/6890

Internet

https://www.mdpi.com/1424-8220/22/18/6890

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