title: Modeling water quality in an urban river using hydrological factors--data driven approaches.
abstract: Contrasting seasonal variations occur in river flow and water quality as a result of short duration, severe intensity storms and typhoons in Taiwan. Sudden changes in river flow caused by impending extreme events may impose serious degradation on river water quality and fateful impacts on ecosystems. Water quality is measured in a monthly/quarterly scale, and therefore an estimation of water quality in a daily scale would be of good help for timely river pollution management. This study proposes a systematic analysis scheme (SAS) to assess the spatio-temporal interrelation of water quality in an urban river and construct water quality estimation models using two static and one dynamic artificial neural networks (ANNs) coupled with the Gamma test (GT) based on water quality, hydrological and economic data. The Dahan River basin in Taiwan is the study area. Ammonia nitrogen (NH3-N) is considered as the representative parameter, a correlative indicator in judging the contamination level over the study. Key factors the most closely related to the representative parameter (NH3-N) are extracted by the Gamma test for modeling NH3-N concentration, and as a result, four hydrological factors (discharge, days w/o discharge, water temperature and rainfall) are identified as model inputs. The modeling results demonstrate that the nonlinear autoregressive with exogenous input (NARX) network furnished with recurrent connections can accurately estimate NH3-N concentration with a very high coefficient of efficiency value (0.926) and a low RMSE value (0.386 mg/l). Besides, the NARX network can suitably catch peak values that mainly occur in dry periods (September-April in the study area), which is particularly important to water pollution treatment. The proposed SAS suggests a promising approach to reliably modeling the spatio-temporal NH3-N concentration based solely on hydrological data, without using water quality sampling data. It is worth noticing that such estimation can be made in a much shorter time interval of interest (span from a monthly scale to a daily scale) because hydrological data are long-term collected in a daily scale. The proposed SAS favorably makes NH3-N concentration estimation much easier (with only hydrological field sampling) and more efficient (in shorter time intervals), which can substantially help river managers interpret and estimate water quality responses to natural and/or manmade pollution in a more effective and timely way for river pollution management.
authors.affiliation: Department of Bioenvironmental Systems Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan, ROC. Electronic address: changfj@ntu.edu.tw.
authors.forename: Fi-John
authors.initials: FJ
authors.lastname: Chang
authors.affiliation: Department of Bioenvironmental Systems Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan, ROC.
authors.forename: Yu-Hsuan
authors.initials: YH
authors.lastname: Tsai
authors.affiliation: Department of Bioenvironmental Systems Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan, ROC.
authors.forename: Pin-An
authors.initials: PA
authors.lastname: Chen
authors.affiliation: Laboratoire d'Environnements et Paléoenvironnements Océaniques et Continentaux, University Bordeaux 1, UMR EPOC, France.
authors.forename: Alexandra
authors.initials: A
authors.lastname: Coynel
authors.affiliation: Laboratoire Transferts en Hydrologie et Environnement, LTHE, UMR 5564 CNRS-IRD-UJF, Grenoble, France.
authors.forename: Georges
authors.initials: G
authors.lastname: Vachaud
mesh_terms: D004784:Environmental Monitoring / Q000379:methods*; D006801:Humans; D062070:Hydrology; D008962:Models, Theoretical; D009584:Nitrogen / Q000737:chemistry; D012044:Regression Analysis; D045483:Rivers; D012621:Seasons; D013624:Taiwan; D014874:Water Pollutants, Chemical / Q000737:chemistry; D060753:Water Quality
chemical_list: D014874:Water Pollutants, Chemical; D009584:Nitrogen
keywords: Ammonia nitrogen (NH(3)–N); Artificial neural network (ANN); Gamma test; Nonlinear autoregressive with exogenous input (NARX) network; River basin management; Water quality