Evolutionary modeling for streamflow forecasting with minimal datasets: A case study in the West Malian River, China

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Ni2010377,
  author =       "Qingwei Ni and Li Wang2 and Renzhen Ye and 
                 Fenglin Yang and Muttucumaru Sivakumar",
  title =        "Evolutionary modeling for streamflow forecasting with
                 minimal datasets: A case study in the West Malian
                 River, China",
  journal =      "Environmental Engineering Science",
  year =         "2010",
  volume =       "27",
  number =       "5",
  pages =        "377--385",
  month =        may # " 7",
  keywords =     "genetic algorithms, genetic programming, Annual
                 streamflow, Automatic selection, Climatic data, Data
                 sets, Degree of accuracy, GP algorithm, Gray theories,
                 Hydrological process, Large datasets, Measured data,
                 Model relationships, Multi layer perceptron, Multiple
                 linear regression models, Potential impacts,
                 Precipitation, Remote areas, Streamflow forecasting,
                 Training and testing, Water resource management,
                 Algorithms, Climate change, Data flow analysis,
                 Developing countries, Electric loads, Evaporation,
                 Forecasting, Genetic programming, Linear regression,
                 Multi-layers, Rivers, Statistics, Stream flow, Water
                 management, Climate models, accuracy, article, back
                 propagation, China, climate change, controlled study,
                 data analysis, developing country, evolutionary
                 algorithm, forecasting, hydrology, multiple regression,
                 perceptron, prediction, stream (river), water flow,
                 water management, Malia, GP algorithm, Statistical
                 methods, West Malian River",
  ISSN =         "10928758",
  URL =          "http://online.liebertpub.com/doi/abs/10.1089/ees.2009.0082",
  DOI =          "doi:10.1089/ees.2009.0082",
  size =         "11 page",
  abstract =     "A large dataset is generally needed when modelling
                 hydrological processes. However, for developing
                 countries such as China, datasets are often unavailable
                 in remote areas. An attempt to apply a novel genetic
                 programming (GP) technique was made to model the
                 relationship between streamflow of the West Malian
                 River and the impact of climate change in the
                 northeastern part of China. Available annual streamflow
                 and climatic data were used for training and testing of
                 the GP model. Data from the years between 1982 and 2002
                 were used for automatic selection of the model
                 relationship. Prediction of the model was undertaken
                 for the period 2003-2006 and the results were compared
                 with measured data. Predicted annual streamflow of the
                 West Malian River agreed with measured data to an
                 acceptable degree of accuracy even with a small amount
                 of dataset. For comparison, a multilayer perceptron
                 method with back propagation algorithm, a gray theory
                 model, and a multiple linear regression model were
                 selected to conduct the prediction with the same
                 dataset. Results showed that the performance of GP
                 method was generally better than other statistical
                 methods such as multilayer perceptron, gray theory
                 model, and multiple linear regression model. Further,
                 the results also showed that the GP method is a useful
                 tool for water resource management, especially in
                 developing countries, to evaluate the potential impacts
                 of climate change on the streamflow when large datasets
                 are unavailable.",
  affiliation =  "School of Environmental and Biological Science and
                 Technology, Dalian University of Technology, Dalian,
                 China; College of Environmental and Biological
                 Engineering, Shenyang University of Chemical
                 Technology, Shenyang 110142, China; Department of
                 Mathematics, Agricultural University of Huazhong,
                 Wuhan, China; School of Civil, Mining and Environmental
                 Engineering, University of Wollongong, Wollongang, NSW,
                 Australia",
  correspondence_address1 = "Wang, L.; College of Environmental and
                 Biological Engineering, Shenyang University of Chemical
                 Technology, Shenyang 110142, China; email:
                 wanglijohn@hotmail.com",
  language =     "English",
  document_type = "Article",
}

Genetic Programming entries for Qingwei Ni Li Wang2 Renzhen Ye Fenglin Yang Muttucumaru Sivakumar

Citations