Estimating monthly total nitrogen concentration in streams by using artificial neural network

Journal of Environmental Management

Volumn 92, Issue 1, 2011, Pages 172-177

  He, Bin ^a,b   Oki, Taikan ^c   Sun, Fubao ^d   Komori, Daisuke ^c   Kanae, Shinjiro ^e   Wang, Yi ^f   Kim, Hyungjun ^c   Yamazaki, Dai ^c  

^a KYOTO UNIVERSITY   (Japan)

^b KYOTO UNIVERSITY   (Japan)

^c INSTITUTE OF INDUSTRIAL SCIENCE   (Japan)

^d AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^e TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^f UNITED NATIONS UNIVERSITY   (Japan)

Author keywords

Artificial neural network; Land use; Nitrogen concentration; Stream water

Indexed keywords

FORECASTING; FORESTRY; LAND USE; NITROGEN FERTILIZERS; RIVERS; SENSITIVITY ANALYSIS;

ARTIFICIAL NEURAL NETWORK MODELING; CONDITION; ENVIRONMENTAL SYSTEMS; FEED-FORWARD ARTIFICIAL NEURAL NETWORKS; NITROGEN CONCENTRATIONS; NONLINEAR BEHAVIOURS; PRECIPITATION TEMPERATURE; RIVER BASINS; STREAM WATER; TOTAL NITROGEN;

NEURAL NETWORKS;

FERTILIZER; NITROGEN;

ARTIFICIAL NEURAL NETWORK; FERTILIZER APPLICATION; HYDROMETEOROLOGY; LAND USE; NITROGEN; RIVER BASIN; RIVER DISCHARGE; SENSITIVITY ANALYSIS; STREAMWATER; URBAN AREA; WATER RESOURCE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CALIBRATION; CONCENTRATION (PARAMETERS); FOREST; JAPAN; LAND USE; PRECIPITATION; PREDICTION; SENSITIVITY ANALYSIS; STREAM (RIVER); TEMPERATURE; URBAN AREA; VALIDATION STUDY; WASTE WATER MANAGEMENT;

JAPAN;

EID: 77957769268     PISSN: 03014797     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jenvman.2010.09.014     Document Type: Article

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