4.7 Article

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

期刊

REMOTE SENSING
卷 14, 期 3, 页码 -

出版社

MDPI
DOI: 10.3390/rs14030472

关键词

spatial modeling; machine learning; remote sensing; model averaging

资金

  1. German Research Foundation (DFG)
  2. DFG Cluster of Excellence Machine LearningNew Perspectives for Science,EXC 2064/1 [390727645]
  3. University of Tuebingen

向作者/读者索取更多资源

This study tested and evaluated multiple base learners and model averaging techniques for predicting soil properties in central Iran. The results showed that model averaging approaches can improve the predictive accuracy for soil properties, with different techniques performing better for different soil attributes.
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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据