title: Use of neural networks for monitoring surface water quality changes in a neotropical urban stream.
abstract: This paper reports the using of neural networks for water quality analysis in a tropical urban stream before (2002) and after sewerage building and the completion of point-source control-based sanitation program (2003). Mathematical modeling divided water quality data in two categories: (a) input of some in situ water quality variables (temperature, pH, O2 concentration, O2 saturation and electrical conductivity) and (b) water chemical composition (N-NO2(-); N-NO3(-); N-NH4(+) Total-N; P-PO4(3-); K+; Ca2+; Mg+2; Cu2+; Zn2+ and Fe+3) as the output from tested models. Stream water data come from fortnightly sampling in five points along the Ipanema stream (Southeast Brazil, Minas Gerais state) plus two points downstream and upstream Ipanema discharge into Doce River. Once the best models are consistent with variables behavior we suggest that neural networking shows potential as a methodology to enhance guidelines for urban streams restoration, conservation and management.
authors.affiliation: Centro de Ciências Agrárias, Universidade Federal do Espírito Santo-CCA/UFES, Alto Universitário, s/n degrees , Guararema, Alegre, ES 29500-000, Brazil. andreaosc@yahoo.com.br
authors.forename: Andréa Oliveira Souza
authors.initials: AO
authors.lastname: da Costa
authors.forename: Priscila Ferreira
authors.initials: PF
authors.lastname: Silva
authors.forename: Millôr Godoy
authors.initials: MG
authors.lastname: Sabará
authors.forename: Esly Ferreira
authors.initials: EF
authors.lastname: da Costa
mesh_terms: D001938:Brazil; D004784:Environmental Monitoring / Q000379:methods*; D005843:Geography; D016571:Neural Networks, Computer; D045483:Rivers / Q000737:chemistry*; D014329:Tropical Climate; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D014874:Water Pollutants, Chemical