A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 3; p. 472
• Main Authors: Taghizadeh-Mehrjardi, Ruhollah, Khademi, Hossein, Khayamim, Fatemeh, Zeraatpisheh, Mojtaba, Heung, Brandon, Scholten, Thomas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Accuracy; Artificial neural networks; Bayesian analysis; Bayesian theory; Electrical conductivity; Electrical resistivity; Environmental management; Genetic algorithms; Information management; Iran; machine learning; model averaging; Neural networks; Organic matter; Particle size; prediction; Remote sensing; sand fraction; Soil organic matter; Soil properties; Soils; Spatial discrimination; Spatial distribution; spatial modeling; Spatial resolution; Topography; Variables; Iran

This study tested and evaluated a suite of nine individual base learners and seven model averaging techniques for predicting the spatial distribution of soil properties in central Iran. Based on the nested-cross validation approach, the results showed that the artificial neural network and Random Fo...