Improving the control of water treatment plant with remote sensing-based water quality forecasting model

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Chang:2015:ICNSC,
  author =       "N. B. Chang and S. Imen",
  booktitle =    "12th IEEE International Conference on Networking,
                 Sensing and Control (ICNSC)",
  title =        "Improving the control of water treatment plant with
                 remote sensing-based water quality forecasting model",
  year =         "2015",
  pages =        "51--57",
  abstract =     "When Total Organic Carbon (TOC) in the source water is
                 in contact with disinfectants in a drinking water
                 treatment process, it often times causes the formation
                 of disinfection by-products such as Trihalomethanes
                 which have harmful effects on human health. As a result
                 of the potential health risk of Trihalomethanes for
                 drinking water, proper monitoring and forecasting of
                 high TOC episodes in the source water body can be
                 helpful for the operators who are in charge of the
                 decisions when they have to start the removal
                 procedures for TOC in surface water treatment plants.
                 This issue is of great importance in Lake Mead in the
                 United States which provides drinking water for 25
                 million people, while it is considered as an important
                 recreational area and wildlife habitat as well. In this
                 study, artificial neural network, extreme learning
                 machine, and genetic programming are examined using the
                 long-term observations of TOC concentration throughout
                 the lake. Among these models, the model with the best
                 performance was applied in the development of a
                 forecasting model to predict TOC values on a daily
                 basis. The forecasting process is aided by an iterative
                 scheme via updating the daily satellite imagery in
                 concert with retrieving the long-term memory of the
                 past states with nonlinear autoregressive neural
                 network with external input (NARXNET) on a rolling
                 basis onwards. The best input scenario of NARXNET was
                 selected with respect to several statistical indices.
                 Numerical outputs of the forecasting process confirm
                 the fidelity of the iterative scheme in predicting
                 water quality status one day ahead of the time.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICNSC.2015.7116009",
  month =        apr,
  notes =        "Also known as \cite{7116009}",
}

Genetic Programming entries for Ni-Bin Chang Sanaz Imen

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