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A Process Identification Algorithm for Predicting Highway Stormwater Pollutographs
Urban and non-urban stormwater are important sources of pollution in various water bodies including rivers, streams, estuaries, lakes and coastal areas as well as groundwater. One step in testing the effectiveness of best management practices in removing or reducing the pollutions in stormwater in a quantitative way is to predict the concentration of pollutions in the influent water. In this research a stepwise approach is utilized, along with a hybrid genetic algorithm optimization technique, to identify optimal physically-based model for prediction of highway stormwater pollutographs. The algorithm selects examine a variety of models representing various processes involved in the transport of contaminants in highway stormwater and selected the best one in terms of reproducing the data that is used for model calibration and an "unseen" dataset. This is done through a step-wise algorithm in which the model complication is added in each step until the models capability in reproducing the unseen data is not improved. The model is based on advective-dispersive transport; kinetic attachment and detachment of contaminants to the highway surface during the event as is controlled by the flow shear-stress and raindrop impact; and non-linear build-up of contaminants during the dry period. The effect of considering that attached contaminants to occur in one phase versus two phases, in terms of the detachment rate, is also studied.
A Process Identification Algorithm for Predicting Highway Stormwater Pollutographs
Urban and non-urban stormwater are important sources of pollution in various water bodies including rivers, streams, estuaries, lakes and coastal areas as well as groundwater. One step in testing the effectiveness of best management practices in removing or reducing the pollutions in stormwater in a quantitative way is to predict the concentration of pollutions in the influent water. In this research a stepwise approach is utilized, along with a hybrid genetic algorithm optimization technique, to identify optimal physically-based model for prediction of highway stormwater pollutographs. The algorithm selects examine a variety of models representing various processes involved in the transport of contaminants in highway stormwater and selected the best one in terms of reproducing the data that is used for model calibration and an "unseen" dataset. This is done through a step-wise algorithm in which the model complication is added in each step until the models capability in reproducing the unseen data is not improved. The model is based on advective-dispersive transport; kinetic attachment and detachment of contaminants to the highway surface during the event as is controlled by the flow shear-stress and raindrop impact; and non-linear build-up of contaminants during the dry period. The effect of considering that attached contaminants to occur in one phase versus two phases, in terms of the detachment rate, is also studied.
A Process Identification Algorithm for Predicting Highway Stormwater Pollutographs
Massoudieh, A. (author) / Kayhanian, M. (author)
World Environmental and Water Resources Congress 2010 ; 2010 ; Providence, Rhode Island, United States
2010-05-14
Conference paper
Electronic Resource
English
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