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 Rajendra B Magar

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