Diagnosis of the artificial intelligence-based predictions of flow regime in a constructed wetland for stormwater pollution control

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Chang:2015:EI,
  author =       "Ni-Bin Chang and Golam Mohiuddin and 
                 A. James Crawford and Kaixu Bai and Kang-Ren Jin",
  title =        "Diagnosis of the artificial intelligence-based
                 predictions of flow regime in a constructed wetland for
                 stormwater pollution control",
  journal =      "Ecological Informatics",
  volume =       "28",
  pages =        "42--60",
  year =         "2015",
  ISSN =         "1574-9541",
  DOI =          "doi:10.1016/j.ecoinf.2015.05.001",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1574954115000795",
  abstract =     "Monitoring the velocity field and stage variations in
                 heterogeneous aquatic environments, such as constructed
                 wetlands, is critical for understanding hydrodynamic
                 patterns, nutrient removal capacity, and hydrographic
                 impact on the wetland ecosystem. Obtaining low velocity
                 measurements representative of the entire wetland
                 system may be challenging, expensive, and even
                 infeasible in some cases. Data-driven modelling
                 techniques in the computational intelligence regime may
                 provide fast predictions of the velocity field based on
                 a handful of local measurements. They can be a
                 convenient tool to visualize the general spatial and
                 temporal distribution of flow magnitude and direction
                 with reasonable accuracy in case regular hydraulic
                 models suffer from insufficient baseline information
                 and longer run time. In this paper, a comparison
                 between two types of bio-inspired computational
                 intelligence models including genetic programming (GP)
                 and artificial neural network (ANN) models was
                 implemented to estimate the velocity field within a
                 constructed wetland (i.e., the Storm-water Treatment
                 Area in South Florida) in the Everglades, Florida. Two
                 different ANN-based models, including back propagation
                 algorithm and extreme learning machine, were used.
                 Model calibration and validation were driven by data
                 collected from a local sensor network of Acoustic
                 Doppler Velocimeters (ADVs) and weather stations. In
                 general, the two ANN-based models outperformed the GP
                 model in terms of several indices. Findings may improve
                 the design and operation strategies for similar wetland
                 systems.",
  keywords =     "genetic algorithms, genetic programming, Constructed
                 wetland, Stormwater Management, Artificial neural
                 network, Velocity Flow Field, Acoustic Doppler
                 Velocimeter",
}

Genetic Programming entries for Ni-Bin Chang Golam Mohiuddin A James Crawford Kaixu Bai Kang-Ren Jin

Citations