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Reservoir Inflow Forecasting Using Differential Evolution Trained Neural Networks

  • Conference paper
EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 288))

Abstract

This paper presents a study on the application of evolutionary computation and artificial intelligence techniques to forecast inflows into the Vanderkloof reservoir, South Africa for the purpose of planning and management of available water resources. A differential evolution (DE)-trained neural network (DE-NN) was developed to simulate the interaction between reservoir inflow and its causal variables such as precipitation and evaporation. The performance of the DE-NN was evaluated using two performance metrics namely mean absolute percent error (MAPE) and coefficient of determination (R2). Results from this study demonstrated that the DE-NN model was able to substantially represent inflow patterns into the Vanderkloof reservoir, thereby indicating the efficacy of the DE algorithm in producing adequate generalization on unseen datasets. These results further showcase differential evolution algorithm as a potent, viable and promising algorithm for training neural network models for use in the field of water resources management.

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Author information

Authors and Affiliations

  1. Department of Civil Engineering and Surveying, Faculty of Engineering and the Built Environment, Durban University of Technology, P.O. Box 1334, Durban, 4000, South Africa

    Oluwaseun Oyebode & Josiah Adeyemo

Authors
  1. Oluwaseun Oyebode
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  2. Josiah Adeyemo
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Corresponding author

Correspondence to Oluwaseun Oyebode .

Editor information

Editors and Affiliations

  1. Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg

    Alexandru-Adrian Tantar

  2. Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg

    Emilia Tantar

  3. School of Engineering, University of California, Merced, California, USA

    Jian-Qiao Sun

  4. College of Mechanical Engineering, Beijing University of Technology, Beijing, China

    Wei Zhang

  5. Department of Mechanics, Tianjin University, Tianjin, China

    Qian Ding

  6. Depto. Computación, CINVESTAV-IPN, Mexico City, Mexico

    Oliver Schütze

  7. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Michael Emmerich

  8. Université de Bordeaux, Bordeaux, France

    Pierrick Legrand

  9. School of Mathematics and Statistics, University of New South Wales, Sydney, New South Wales, Australia

    Pierre Del Moral

  10. Computer Science Department, CINVESTAV-IPN, Mexico City, Mexico

    Carlos A. Coello Coello

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Oyebode, O., Adeyemo, J. (2014). Reservoir Inflow Forecasting Using Differential Evolution Trained Neural Networks. In: Tantar, AA., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V. Advances in Intelligent Systems and Computing, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-319-07494-8_21

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  • DOI: https://doi.org/10.1007/978-3-319-07494-8_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07493-1

  • Online ISBN: 978-3-319-07494-8

  • eBook Packages: EngineeringEngineering (R0)

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