Artificial neural networks and support vector machines: Contrast study for groundwater level prediction

American Society of Agricultural and Biological Engineers Annual International Meeting 2015

Volumn 2, Issue , 2015, Pages 998-1008

  Guzman, Sandra M ^a   Paz, Joel O ^a   Tagert, Mary Love M ^a   Mercer, Andrew ^b  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

^b MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

Forecasting; Groundwater; Irrigation; Neural networks; Support vector machine; Support vector regression; Water efficiency and modeling

Indexed keywords

AGRICULTURAL MACHINERY; AGRICULTURE; AQUIFERS; ARTIFICIAL INTELLIGENCE; CULTIVATION; DECISION SUPPORT SYSTEMS; EFFICIENCY; FORECASTING; GROUNDWATER; GROUNDWATER RESOURCES; IRRIGATION; LEARNING SYSTEMS; MEAN SQUARE ERROR; NEURAL NETWORKS; OPTIMIZATION; SUPPORT VECTOR MACHINES; VECTORS; WATER SUPPLY SYSTEMS;

AGRICULTURAL PRODUCTIONS; GROUNDWATER CONSERVATION; GROUNDWATER FORECASTING; MACHINE LEARNING TECHNIQUES; OPTIMIZATION EFFICIENCY; STATISTICAL LEARNING THEORY; SUPPORT VECTOR REGRESSION (SVR); WATER EFFICIENCY;

LEARNING ALGORITHMS;

EID: 84951806553     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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