Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point

Soil and Tillage Research

Volumn 172, Issue , 2017, Pages 32-38

  Ghorbani, Mohammad Ali ^a,b   Shamshirband, Shahaboddin ^c   Zare Haghi, Davoud ^a   Azani, Atefe ^a   Bonakdari, Hossein ^d   Ebtehaj, Isa ^d  

^a UNIVERSITY OF TABRIZ   (Iran)

^b NEAR EAST UNIVERSITY   (Turkey)

^c TON DUC THANG UNIVERSITY   (Viet Nam)

^d RAZI UNIVERSITY   (Iran)

Author keywords

Field capacity; Hybrid model; Permanent wilting point

Indexed keywords

AGRICULTURAL MACHINERY; BIOLUMINESCENCE; DATA FLOW ANALYSIS; FORECASTING; MEAN SQUARE ERROR; NEURAL NETWORKS; OPTIMIZATION; SOIL SURVEYS; SOILS; STATISTICAL TESTS;

AGRICULTURAL ACTIVITIES; ARTIFICIAL NEURAL NETWORK MODELS; CORRELATION COEFFICIENT; FIELD CAPACITY; HYBRID MODEL; HYBRID SUPPORT VECTOR MACHINES; PERMANENT WILTING POINTS; ROOT MEAN SQUARE ERRORS;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BULK DENSITY; DATA SET; FIELD CAPACITY; NUMERICAL MODEL; ORGANIC MATTER; SOIL TYPE; SUPPORT VECTOR MACHINE; WILTING;

EAST AZERBAIJAN; IRAN;

EID: 85019152630     PISSN: 01671987     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.still.2017.04.009     Document Type: Article

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