title: ANN application for prediction of atmospheric nitrogen deposition to aquatic ecosystems.
abstract: The occurrences of increased atmospheric nitrogen deposition (ADN) in Southeast Asia during smoke haze episodes have undesired consequences on receiving aquatic ecosystems. A successful prediction of episodic ADN will allow a quantitative understanding of its possible impacts. In this study, an artificial neural network (ANN) model is used to estimate atmospheric deposition of total nitrogen (TN) and organic nitrogen (ON) concentrations to coastal aquatic ecosystems. The selected model input variables were nitrogen species from atmospheric deposition, Total Suspended Particulates, Pollutant Standards Index and meteorological parameters. ANN models predictions were also compared with multiple linear regression model having the same inputs and output. ANN model performance was found relatively more accurate in its predictions and adequate even for high-concentration events with acceptable minimum error. The developed ANN model can be used as a forecasting tool to complement the current TN and ON analysis within the atmospheric deposition-monitoring program in the region.
authors.affiliation: Tropical Marine Science Institute, National University of Singapore, 18 Kent Ridge Road, Singapore 119227, Singapore. tmssp@nus.edu.sg
authors.forename: Sundarambal
authors.initials: S
authors.lastname: Palani
authors.forename: Pavel
authors.initials: P
authors.lastname: Tkalich
authors.forename: Rajasekhar
authors.initials: R
authors.lastname: Balasubramanian
authors.forename: Jegathambal
authors.initials: J
authors.lastname: Palanichamy
mesh_terms: D000393:Air Pollutants / Q000032:analysis; D000397:Air Pollution / Q000706:statistics & numerical data*; D001272:Atmosphere / Q000737:chemistry*; D003198:Computer Simulation; D004784:Environmental Monitoring / Q000295:instrumentation / Q000379:methods*; D005068:Eutrophication; D016014:Linear Models; D008956:Models, Chemical; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000032:analysis*; D014874:Water Pollutants, Chemical / Q000032:analysis; D014877:Water Pollution, Chemical / Q000706:statistics & numerical data*
chemical_list: D000393:Air Pollutants; D014874:Water Pollutants, Chemical; D009584:Nitrogen