Application of artificial intelligence models in water quality forecasting

Environmental Technology

Volumn 29, Issue 6, 2008, Pages 625-631

  Yeon, I S ^a   Kim, J H ^b   Jun, K W ^c  

^a Chungbuk National University   (South Korea)

^b Korea Institute of Civil Engineering and Building Technology   (South Korea)

^c Kangwon National University   (South Korea)

Author keywords

ANFIS (adaptive neuro fuzzy inference system); Forecasting; Monitoring; Neural networks; Water quality

Indexed keywords

BIOCHEMICAL OXYGEN DEMAND; DISSOLVED OXYGEN; FORECASTING; FUZZY INFERENCE; FUZZY SYSTEMS; MONITORING; NEURAL NETWORKS; ORGANIC CARBON; WATER QUALITY;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ANFIS (ADAPTIVE NEURO FUZZY INFERENCE SYSTEM); LEVENBERG-MARQUARDT; MODULAR NEURAL NETWORKS; NEURAL NETWORK MODEL; STANDARD DEVIATION; TOTAL ORGANIC CARBON; WATER QUALITY MONITORING STATIONS;

FUZZY NEURAL NETWORKS;

DISSOLVED OXYGEN; ORGANIC CARBON;

MATHEMATICAL MODELS; ORGANIC CARBON; REAL TIME SYSTEMS; RIVERS; WATER QUALITY;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; DISSOLVED OXYGEN; ENVIRONMENTAL MONITORING; FORECASTING METHOD; MODELING; PRECIPITATION (CLIMATOLOGY); REAL TIME; RIVER WATER; TOTAL ORGANIC CARBON; WATER QUALITY;

ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; ENVIRONMENTAL MONITORING; FORECASTING; QUALITATIVE ANALYSIS; RIVER; STATISTICAL ANALYSIS; TOTAL ORGANIC CARBON; WATER QUALITY;

ASIA; EURASIA; FAR EAST; KOREA; SOUTH KOREA;

EID: 45849090754     PISSN: 09593330     EISSN: 1479487X     Source Type: Journal    
DOI: 10.1080/09593330801984456     Document Type: Article

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