Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data

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@Article{Jothiprakash2012293,
  author =       "V. Jothiprakash and R. B. Magar",
  title =        "Multi-time-step ahead daily and hourly intermittent
                 reservoir inflow prediction by artificial intelligent
                 techniques using lumped and distributed data",
  journal =      "Journal of Hydrology",
  volume =       "450-451",
  month =        "11 " # jul,
  pages =        "293--307",
  year =         "2012",
  ISSN =         "0022-1694",
  DOI =          "doi:10.1016/j.jhydrol.2012.04.045",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0022169412003459",
  keywords =     "genetic algorithms, genetic programming, Time-series
                 models, Cause-effect models, Combined models, Daily and
                 hourly, Lumped and distributed data, Artificial
                 intelligent techniques",
  abstract =     "In this study, artificial intelligent (AI) techniques
                 such as artificial neural network (ANN), Adaptive
                 neuro-fuzzy inference system (ANFIS) and Linear genetic
                 programming (LGP) are used to predict daily and hourly
                 multi-time-step ahead intermittent reservoir inflow. To
                 illustrate the applicability of AI techniques,
                 intermittent Koyna river watershed in Maharashtra,
                 India is chosen as a case study. Based on the observed
                 daily and hourly rainfall and reservoir inflow various
                 types of time-series, cause-effect and combined models
                 are developed with lumped and distributed input data.
                 Further, the model performance was evaluated using
                 various performance criteria. From the results, it is
                 found that the performances of LGP models are found to
                 be superior to ANN and ANFIS models especially in
                 predicting the peak inflows for both daily and hourly
                 time-step. A detailed comparison of the overall
                 performance indicated that the combined input model
                 (combination of rainfall and inflow) performed better
                 in both lumped and distributed input data modelling. It
                 was observed that the lumped input data models
                 performed slightly better because; apart from reducing
                 the noise in the data, the better techniques and their
                 training approach, appropriate selection of network
                 architecture, required inputs, and also
                 training-testing ratios of the data set. The slight
                 poor performance of distributed data is due to large
                 variations and lesser number of observed values.",
}

Genetic Programming entries for V Jothiprakash R B Magar

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