Identification of land use with water quality data in stormwater using a neural network.
To control stormwater pollution effectively, development of innovative, land-use-related control strategies will be required. An approach that could differentiate land-use types from stormwater quality would be the first step to solving this problem. We propose a neural network approach to examine the relationship between stormwater water quality and various types of land use. The neural network model can be used to identify land-use types for future known and unknown cases. The neural model uses a Bayesian network and has 10 water quality input variables, four neurons in the hidden layer, and five land-use target variables (commercial, industrial, residential, transportation, and vacant). We obtained 92.3 percent of correct classification and 0.157 root-mean-squared error on test files. Based on the neural model, simulations were performed to predict the land-use type of a known data set, which was not used when developing the model. The simulation accurately described the behavior of the new data set. This study demonstrates that a neural network can be effectively used to produce land-use type classification with water quality data.
Ha
Haejin
H
Department of Civil and Environmental Engineering, University of California, 5714 Boelter Hall, Los Angeles, Los Angeles, CA 90095, USA.
Stenstrom
Michael K
MK
D000383:Agriculture; D001499:Bayes Theorem; D004784:Environmental Monitoring; D005544:Forecasting; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011786:Quality Control; D011891:Rain; D014872:Water Movements; D014873:Water Pollutants / Q000032:analysis*
D014873:Water Pollutants