A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties
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...
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MDPI AG
2022-01-01
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Online Access: | https://www.mdpi.com/2072-4292/14/3/472 |
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author | Ruhollah Taghizadeh-Mehrjardi Hossein Khademi Fatemeh Khayamim Mojtaba Zeraatpisheh Brandon Heung Thomas Scholten |
author_facet | Ruhollah Taghizadeh-Mehrjardi Hossein Khademi Fatemeh Khayamim Mojtaba Zeraatpisheh Brandon Heung Thomas Scholten |
author_sort | Ruhollah Taghizadeh-Mehrjardi |
collection | DOAJ |
description | 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 Forest base learners were the most effective in predicting soil organic matter and electrical conductivity, respectively. However, all seven model averaging techniques performed better than the base learners. For example, the Granger–Ramanathan averaging approach resulted in the highest prediction accuracy for soil organic matter, while the Bayesian model averaging approach was most effective in predicting sand content. These results indicate that the model averaging approaches could improve the predictive accuracy for soil properties. The resulting maps, produced at a 30 m spatial resolution, can be used as valuable baseline information for managing environmental resources more effectively. |
first_indexed | 2024-03-09T23:14:59Z |
format | Article |
id | doaj.art-b876e62398984d578a78e810d775fd14 |
institution | Directory Open Access Journal |
issn | 2072-4292 |
language | English |
last_indexed | 2024-03-09T23:14:59Z |
publishDate | 2022-01-01 |
publisher | MDPI AG |
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series | Remote Sensing |
spelling | doaj.art-b876e62398984d578a78e810d775fd142023-11-23T17:38:11ZengMDPI AGRemote Sensing2072-42922022-01-0114347210.3390/rs14030472A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil PropertiesRuhollah Taghizadeh-Mehrjardi0Hossein Khademi1Fatemeh Khayamim2Mojtaba Zeraatpisheh3Brandon Heung4Thomas Scholten5Department of Geosciences, Soil Science and Geomorphology, University of Tübingen, 72070 Tübingen, GermanyDepartment of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan 8415683111, IranDepartment of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan 8415683111, IranHenan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, ChinaDepartment of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS B2N 5E3, CanadaDepartment of Geosciences, Soil Science and Geomorphology, University of Tübingen, 72070 Tübingen, GermanyThis 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 Forest base learners were the most effective in predicting soil organic matter and electrical conductivity, respectively. However, all seven model averaging techniques performed better than the base learners. For example, the Granger–Ramanathan averaging approach resulted in the highest prediction accuracy for soil organic matter, while the Bayesian model averaging approach was most effective in predicting sand content. These results indicate that the model averaging approaches could improve the predictive accuracy for soil properties. The resulting maps, produced at a 30 m spatial resolution, can be used as valuable baseline information for managing environmental resources more effectively.https://www.mdpi.com/2072-4292/14/3/472spatial modelingmachine learningremote sensingmodel averaging |
spellingShingle | Ruhollah Taghizadeh-Mehrjardi Hossein Khademi Fatemeh Khayamim Mojtaba Zeraatpisheh Brandon Heung Thomas Scholten A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties Remote Sensing spatial modeling machine learning remote sensing model averaging |
title | A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties |
title_full | A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties |
title_fullStr | A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties |
title_full_unstemmed | A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties |
title_short | A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties |
title_sort | comparison of model averaging techniques to predict the spatial distribution of soil properties |
topic | spatial modeling machine learning remote sensing model averaging |
url | https://www.mdpi.com/2072-4292/14/3/472 |
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