Two hybrid Artificial Intelligence approaches for modeling rainfall-runoff process

Journal of Hydrology

Volumn 402, Issue 1-2, 2011, Pages 41-59

  Nourani, Vahid ^a,c   Kisi, Özgür ^b   Komasi, Mehdi ^a  

^a UNIVERSITY OF TABRIZ   (Iran)

^b ERCIYES UNIVERSITY   (Turkey)

^c UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Artificial Neural Network; Lighvanchai and Aghchai watersheds; SARIMAX; Wavelet transform

Indexed keywords

ACCURATE MODELING; ADAPTIVE NEURAL FUZZY INFERENCE SYSTEMS; ANFIS MODEL; ARTIFICIAL NEURAL NETWORK; AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; AZERBAIJAN; EXOGENOUS INPUT; INPUT DATAS; INPUT LAYERS; LIGHVANCHAI AND AGHCHAI WATERSHEDS; LONG TERM; MULTI FREQUENCY; MULTISCALES; NON-LINEAR RELATIONSHIPS; RAINFALL-RUNOFF MODELING; RAINFALL-RUNOFF PROCESS; RUNOFF DATA; RUNOFF DISCHARGE; SARIMAX; SEASONALITY; STOCHASTIC PROPERTIES; TIME STEP;

FUZZY INFERENCE; LANDFORMS; MATHEMATICAL TRANSFORMATIONS; NEURAL NETWORKS; RUNOFF; STOCHASTIC MODELS; TIME SERIES; WATERSHEDS; WAVELET TRANSFORMS;

RAIN;

ACCURACY ASSESSMENT; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; DISCHARGE; FREQUENCY ANALYSIS; LINEARITY; NUMERICAL MODEL; RAINFALL-RUNOFF MODELING; SEASONALITY; STOCHASTICITY; TIME SERIES; WATERSHED; WAVELET;

EID: 79955025170     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2011.03.002     Document Type: Article

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