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,
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author = "V. Jothiprakash and R. B. Magar",
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title = "Multi-time-step ahead daily and hourly intermittent
reservoir inflow prediction by artificial intelligent
techniques using lumped and distributed data",
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journal = "Journal of Hydrology",
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volume = "450-451",
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month = "11 " # jul,
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pages = "293--307",
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year = "2012",
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ISSN = "0022-1694",
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DOI = "doi:10.1016/j.jhydrol.2012.04.045",
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URL = "http://www.sciencedirect.com/science/article/pii/S0022169412003459",
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keywords = "genetic algorithms, genetic programming, Time-series
models, Cause-effect models, Combined models, Daily and
hourly, Lumped and distributed data, Artificial
intelligent techniques",
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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
Rajendra B Magar
Citations