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.

Study of heavy metals in urban runoff

“…In addition, the quality and quantity of watershed runoff are strongly dependent on land use and land cover (LULC) and the unit pollutant load applied. Using LULC information, environmental modeling has become much easier in recent years due to the increased availability of a dataset, and the improved ability to manage and visualize geospatial information in geographic Information System (GIS) (Ha, 2003 andPapiri andCiaponi, 2003). Urban hydrology is one of the areas where knowledge, of inevitably both environmental sciences and environmental engineering are necessary when strategies for the optimum solution of environmental problems have to be developed (Allen, et al, 2002 andJurgen, 1996).…”

Smart management of combined sewer overflows: From an ancient technology to artificial intelligence

“…The flood monitoring and damage estimation relied heavily on these curves (Cermak et al, 1979). In another paper, radial-basis-function neural network (RBF-NN) and the ANN artificial intelligence techniques were applied on panchromatic imageries of Landsat thematic mapper (Landsat TM) and Korea multi-purpose satellite (KOMPSAT) in land use/cover classification in an area in Korea (Ha et al, 2003). The outcome was exerted as input for SWMM to predict stormwater runoff quantity and biological oxygen demand (BOD) loading.…”

Development of detailed distributed urban drainage models using remote sensing and GIS data.

“…Particularly in water quality and quantity modelling, some of the studies demonstrated the use of remote sensing data to retrieve land cover information. For example, Elgy (2001) used airborne data to classify land cover types which was aimed to be used in urban drainage modelling; Thanapura et al (2007) extracted the runoff coefficient from QuickBird and GIS vector layers; water quality models were developed by Ha et al (2003) from Landsat TM multispectral bands and Korea Multi-Purpose Satellite (KOMPSAT) panchromatic data, and Lee et al (2010) from QuickBird satellite imagery.…”