Evaluation of parametric and nonparametric machine-learning techniques for prediction of saturated and near-saturated hydraulic conductivity (2019)
- Authors:
- USP affiliated author: LIER, QUIRIJN DE JONG VAN - CENA
- School: CENA
- DOI: 10.2136/vzj2018.07.0141
- Subject: CONDUTIVIDADE HIDRÁULICA DO SOLO
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Vadose Zone Journal
- Volume/Número/Paginação/Ano: v. 18, n. 1, 2019
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by-nc-nd
-
ABNT
MEHMANDOOST, Ali e LIER, Quirijn de Jong van e IVERSEN, Bo V. Evaluation of parametric and nonparametric machine-learning techniques for prediction of saturated and near-saturated hydraulic conductivity. Vadose Zone Journal, v. 18, n. 1, 2019Tradução . . Acesso em: 04 jul. 2022. -
APA
Mehmandoost, A., Lier, Q. de J. van, & Iversen, B. V. (2019). Evaluation of parametric and nonparametric machine-learning techniques for prediction of saturated and near-saturated hydraulic conductivity. Vadose Zone Journal, 18( 1). doi:10.2136/vzj2018.07.0141 -
NLM
Mehmandoost A, Lier Q de J van, Iversen BV. Evaluation of parametric and nonparametric machine-learning techniques for prediction of saturated and near-saturated hydraulic conductivity. Vadose Zone Journal. 2019 ; 18( 1):[citado 2022 jul. 04 ] -
Vancouver
Mehmandoost A, Lier Q de J van, Iversen BV. Evaluation of parametric and nonparametric machine-learning techniques for prediction of saturated and near-saturated hydraulic conductivity. Vadose Zone Journal. 2019 ; 18( 1):[citado 2022 jul. 04 ] - Pressure heads and simulated water uptake patterns for a severely stressed ban crop
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Informações sobre o DOI: 10.2136/vzj2018.07.0141 (Fonte: oaDOI API)
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