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- W2322969665 abstract "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." @default.
- W2322969665 created "2016-06-24" @default.
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- W2322969665 date "2010-05-14" @default.
- W2322969665 modified "2023-09-26" @default.
- W2322969665 title "A Process Identification Algorithm for Predicting Highway Stormwater Pollutographs" @default.
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- W2322969665 doi "https://doi.org/10.1061/41114(371)319" @default.
- W2322969665 hasPublicationYear "2010" @default.
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