Predicting high resolution total phosphorus concentrations for soils of the Upper Mississippi River Basin using machine learning
Author:
Funder
Natural Resources Conservation Service
Publisher
Springer Science and Business Media LLC
Subject
Earth-Surface Processes,Water Science and Technology,Environmental Chemistry
Link
https://link.springer.com/content/pdf/10.1007/s10533-023-01029-8.pdf
Reference73 articles.
1. Allen BL, Mallarino AP (2006) Relationships between extractable soil phosphorus and phosphorus saturation after long-term fertilizer or manure application. Soil Sci Soc Am J 70:454–463. https://doi.org/10.2136/sssaj2005.0031
2. Berhe AA, Barnes RT, Six J, Marín-Spiotta E (2018) Role of soil erosion in biogeochemical cycling of essential elements: Carbon, nitrogen, and phosphorus. Annu Rev Earth Planet Sci 46:521–548. https://doi.org/10.1146/annurev-earth-082517-010018USGS
3. Boardman E, Danesh-Yazdi M, Foufoula-Georgiou E, Dolph CL, Finlay JC (2019) Fertilizer, landscape features and climate regulate phosphorus retention and river export in diverse Midwestern watersheds. Biogeochemistry 146:293–309. https://doi.org/10.1007/s10533-019-00623-z
4. Breiman L (2001) Random Forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
5. Chicco D, Warrens MJ, Jurman G (2021) The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput Sci 7:e623. https://doi.org/10.7717/peerj-cs.623
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