Tree-Based Modeling Methods to Predict Nitrate Exceedances in the Ogallala Aquifer in Texas

The performance of four tree-based classification techniques—classification and regression trees (CART), multi-adaptive regression splines (MARS), random forests (RF) and gradient boosting trees (GBT) were compared against the commonly used logistic regression (LR) analysis to assess aquifer vulnera...

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Vydané v:	Water (Basel) Ročník 12; číslo 4; s. 1023
Hlavní autori:	Uddameri, Venkatesh, Silva, Ana, Singaraju, Sreeram, Mohammadi, Ghazal, Hernandez, E.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.04.2020
Predmet:	Advantages agriculture Algorithms alkalinity animals Aquifers area calcium carbonate classification clay fraction data collection Datasets decision support systems Drinking water Environmental aspects feedlots Groundwater Hydrogeology Machine learning monitoring Nitrates Nitrogen prediction Random variables regression analysis soil organic matter Texas Trees water Water quality United States United States > US Texas
ISSN:	2073-4441, 2073-4441
On-line prístup:	Získať plný text
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