title: Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysis.
abstract: We propose a systematical approach to assessing arsenic concentration in a river through: important factor extraction by a nonlinear factor analysis; arsenic concentration estimation by the neuro-fuzzy network; and impact assessment of important factors on arsenic concentration by the membership degrees of the constructed neuro-fuzzy network. The arsenic-contaminated Huang Gang Creek in northern Taiwan is used as a study case. Results indicate that rainfall, nitrite nitrogen and temperature are important factors and the proposed estimation model (ANFIS(GT)) is superior to the two comparative models, in which 50% and 52% improvements in RMSE are made over ANFIS(CC) and ANFIS(all), respectively. Results reveal that arsenic concentration reaches the highest in an environment of lower temperature, higher nitrite nitrogen concentration and larger one-month antecedent rainfall; while it reaches the lowest in an environment of higher temperature, lower nitrite nitrogen concentration and smaller one-month antecedent rainfall. It is noted that these three selected factors are easy-to-collect. We demonstrate that the proposed methodology is a useful and effective methodology, which can be adapted to other similar settings to reliably model water quality based on parameters of interest and/or study areas of interest for universal usage. The proposed methodology gives a quick and reliable way to estimate arsenic concentration, which makes good contribution to water environment management.
authors.affiliation: Department of Bioenvironmental Systems Engineering, National Taiwan University, 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, Taipei 10617, Taiwan, ROC.
authors.forename: Chang-Han
authors.initials: CH
authors.lastname: Chung
authors.affiliation: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan, ROC.
authors.forename: Pin-An
authors.initials: PA
authors.lastname: Chen
authors.affiliation: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan, ROC.
authors.forename: Chen-Wuing
authors.initials: CW
authors.lastname: Liu
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: D001151:Arsenic / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D005163:Factor Analysis, Statistical; D017143:Fuzzy Logic; D016571:Neural Networks, Computer; D012044:Regression Analysis; D045483:Rivers / Q000737:chemistry*; D013624:Taiwan; D014874:Water Pollutants, Chemical / Q000032:analysis; D014877:Water Pollution, Chemical / Q000706:statistics & numerical data*
chemical_list: D014874:Water Pollutants, Chemical; D001151:Arsenic
keywords: Adaptive network-based fuzzy inference system (ANFIS); Arsenic; Gamma test; Neuro-fuzzy network; Water quality