A Soil Bulk Density Pedotransfer Function Based on Machine Learning: A Case Study With The Kellogg Soil Survey Laboratory Database
收藏DataONE2017-06-22 更新2024-06-26 收录
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Soils data from 41,878 horizons were extracted from the Kellogg Soil Survey Laboratory (KSSL) database and used to calibrate and validate the PTF. Environmental datasets included terrain attributes (elevation, slope, aspect, landform), national land cover classification, hierarchical ecosystem land classifications, and 19 bioclimatic indicators. The results of a 5‒fold cross-validation scheme showed that average root mean squared prediction error (RMSPE) was 0.13 g cm-3, and mean prediction error (MPE) was -0.001 g cm-3.
创建时间：
2023-11-21
