title: Estimation of urban runoff and water quality using remote sensing and artificial intelligence.
abstract: Water quality and quantity of runoff are strongly dependent on the landuse and landcover (LULC) criteria. In this study, we developed a more improved parameter estimation procedure for the environmental model using remote sensing (RS) and artificial intelligence (AI) techniques. Landsat TM multi-band (7bands) and Korea Multi-Purpose Satellite (KOMPSAT) panchromatic data were selected for input data processing. We employed two kinds of artificial intelligence techniques, RBF-NN (radial-basis-function neural network) and ANN (artificial neural network), to classify LULC of the study area. A bootstrap resampling method, a statistical technique, was employed to generate the confidence intervals and distribution of the unit load. SWMM was used to simulate the urban runoff and water quality and applied to the study watershed. The condition of urban flow and non-point contaminations was simulated with rainfall-runoff and measured water quality data. The estimated total runoff, peak time, and pollutant generation varied considerably according to the classification accuracy and percentile unit load applied. The proposed procedure would efficiently be applied to water quality and runoff simulation in a rapidly changing urban area.
authors.affiliation: Department of Urban Engineering, Chungbuk National University, Republic of Korea. simplet@cbucc.chungbuk.ac.kr
authors.forename: S R
authors.initials: SR
authors.lastname: Ha
authors.forename: S Y
authors.initials: SY
authors.lastname: Park
authors.forename: D H
authors.initials: DH
authors.lastname: Park
mesh_terms: D001185:Artificial Intelligence; D002947:Cities; D004784:Environmental Monitoring / Q000379:methods*; D008962:Models, Theoretical; D011891:Rain; D012680:Sensitivity and Specificity; D018531:Spacecraft; D014872:Water Movements; D014873:Water Pollutants / Q000032:analysis*
chemical_list: D014873:Water Pollutants