Artificial Neural Networks (ANN) models are used to predict residual chlorine, substrate and biomass concentrations in a Water Distribution System (WDS). ANN models with different architectures are developed: a one output ANN model (predicting chlorine, substrate and biomass individually), a two output ANN model (predicting chlorine + substrate, chlorine + biomass or substrate + biomass) and a three output ANN model (chlorine + substrate + biomass). This study is carried out for the Bangalore City and North Marin WDSs. Data for these WDSs is obtained from the multi-component reaction transport model. The models are compared using the correlation coefficient (R) and the Mean Absolute Error (MAE). The models developed are able to predict, reasonably well, the temporal variations in the chlorine, substrate and biomass concentrations. Error analysis is carried out to determine the robustness of the models.
Skip Nav Destination
Article navigation
Research Article|
August 01 2009
Prediction of multi-components (chlorine, biomass and substrate concentrations) in water distribution systems using artificial neural network (ANN) models Available to Purchase
Celia D. D'Souza;
1Department of Civil Engineering, Indian Institute of Science, Bangalore 560 012, India E-mail: [email protected]
E-mail: [email protected]
Search for other works by this author on:
M. S. Mohan Kumar
M. S. Mohan Kumar
1Department of Civil Engineering, Indian Institute of Science, Bangalore 560 012, India E-mail: [email protected]
Search for other works by this author on:
Water Supply (2009) 9 (3): 289–297.
Citation
Celia D. D'Souza, M. S. Mohan Kumar; Prediction of multi-components (chlorine, biomass and substrate concentrations) in water distribution systems using artificial neural network (ANN) models. Water Supply 1 August 2009; 9 (3): 289–297. doi: https://doi.org/10.2166/ws.2009.464
Download citation file:
Sign in
Don't already have an account? Register
Client Account
You could not be signed in. Please check your email address / username and password and try again.
Could not validate captcha. Please try again.
eBook
Pay-Per-View Access
$38.00