Use of Arti cial Neural Networks in Predicting Highway Runo Constituent Event Mean Concentration


1 Department of Civil and Environmental Engineering,University of California

2 Department of Civil and Environmental Engineering,Davis


In this paper, the large amount of highway runo characterization data that were collected in
California, during a 3-year monitoring season (2000-2003), were assessed in order to develop an
Arti cial Neural Network (ANN) model for predicting the Event Mean Concentration (EMC)
of the constituent. The initial data analysis performed by a Multiple Linear Regression (MLR)
model revealed that the Total Event Rainfall (TER), the Cumulative Seasonal Rainfall (CSR), the
Antecedent Dry Period (ADP), the contributing Drainage Area (DA) and the Annual Average
Daily Trac (AADT) were among the variables having a signi cant impact on the highway
runo constituent EMC. These parameters were used as the basis for developing an Arti cial
Neural Network (ANN) model. The ANN model was also used to evaluate the impact of various
site and storm event variables on highway runo constituents' EMCs. The ANN model has
proven to be superior to the previously developed MLR model, with an improved R2 for most
constituents. Through the ANN model, one was able to see some non-linear e ects of multi
variables on pollutant concentration that, otherwise, would not have been possible with a typical
MLR model. For example, the results showed that copper EMC is more sensitive at higher
Annual Average Daily Trac (AADT), with respect to ADP, compared with lower range AADT.