Improving water content estimations using penetration resistance and principal component analysis

Soil and Tillage Research

Volumn 129, Issue , 2013, Pages 83-92

  Bayat, Hossein ^a   Neyshaburi, Mohammad Reza ^b   Mohammadi, Kourosh ^c   Nariman Zadeh, Nader ^d   Irannejad, Mahdi ^e  

^a BU ALI SINA UNIVERSITY   (Iran)

^b UNIVERSITY OF TABRIZ   (Iran)

^c TARBIAT MODARES UNIVERSITY   (Iran)

^d UNIVERSITY OF GUILAN   (Iran)

^e AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Artificial neural networks; Estimation; Penetration resistance; Principal components; Water retention

Indexed keywords

AS-SOILS; FIELD CAPACITY; MACRO-POROSITY; MATRIC SUCTIONS; MEAN WEIGHT DIAMETER; PEDO-TRANSFER FUNCTIONS; PENETRATION RESISTANCES; PRELIMINARY ANALYSIS; PRINCIPAL COMPONENTS; SATURATED HYDRAULIC CONDUCTIVITY; SOIL DATABASE; SOIL PHYSICAL PROPERTY; SOIL SAMPLE; SOIL WATER RETENTION CURVES; WATER RETENTION;

NEURAL NETWORKS; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; SOIL MOISTURE;

ESTIMATION;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; DATABASE; HYDRAULIC CONDUCTIVITY; MACROPORE; PATTERN RECOGNITION; PEDOTRANSFER FUNCTION; PENETRATION; PHYSICAL PROPERTY; PORE SPACE; PRINCIPAL COMPONENT ANALYSIS; RELIABILITY ANALYSIS; SOIL WATER; WATER COLUMN; WATER RETENTION;

IRAN;

EID: 84874443569     PISSN: 01671987     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.still.2013.01.009     Document Type: Article

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