Groundwater budget forecasting, using hybrid wavelet-ANN-GP modelling: A case study of Azarshahr Plain, East Azerbaijan, Iran

Hydrology Research

Volumn 48, Issue 2, 2017, Pages 455-467

  Gorgij, Alireza Docheshmeh ^a   Kisi, Ozgur ^b   Moghaddam, Asghar Asghari ^a  

^a UNIVERSITY OF TABRIZ   (Iran)

^b CANIK BASARI UNIVERSITY   (Turkey)

Author keywords

Artificial intelligence; Azarshahr; Genetic programming; Groundwater budget; Performance evaluation; Wavelet artificial neural network

Indexed keywords

AQUIFERS; ARTIFICIAL INTELLIGENCE; BUDGET CONTROL; DEEP NEURAL NETWORKS; ENVIRONMENTAL MANAGEMENT; FORECASTING; GENETIC ALGORITHMS; GENETIC PROGRAMMING; GROUNDWATER; MEAN SQUARE ERROR; NEURAL NETWORKS; WAVELET ANALYSIS;

ARID AND SEMI-ARID AREAS; AZARSHAHR; HYDROLOGICAL PROCESS; MEAN ABSOLUTE ERROR; PERFORMANCE EVALUATION; PERFORMANCE EVALUATION CRITERIA; ROOT MEAN SQUARED ERRORS; STORAGE COEFFICIENT;

GROUNDWATER RESOURCES;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; ENVIRONMENTAL PLANNING; GENETIC ALGORITHM; GROUNDWATER; GROUNDWATER RESOURCE; HYDROLOGICAL MODELING; SEMIARID REGION; UNIT HYDROGRAPH; WATER BUDGET; WAVELET ANALYSIS;

EAST AZERBAIJAN; IRAN;

EID: 85016634184     PISSN: 19989563     EISSN: 22247955     Source Type: Journal    
DOI: 10.2166/nh.2016.202     Document Type: Article

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