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International Conference on Neural Information Processing

ICONIP 2018: Neural Information Processing pp 58–69Cite as

Applying Macroclimatic Variables to Improve Flow Rate Forecasting Using Neural Networks Techniques

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11305))

Abstract

Since 2013, the São Francisco River has being through a low hydraulicity period. In other words, the rain intensity is below average. Consequently, it has being necessary to operate at a minimal flow rate. It is far below the ones established at the operation licence, which is 1300 m\(^3\)/s. Due to this hydraulic crisis, the actual operational flow rate is 700 m\(^3\)/s at São Francisco River, characterizing this situation as critical. In this work, it was proposed to use Reservoir Computing (RC), Long Short Term Memory (LSTM) and Deep Learning to predict Sobradinho’s flow rate for 1, 2 and 3 months ahead using macroclimatic variables. After having the results for each one of them, a comparison was made and statistical tests where executed for evaluation.

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Author information

Authors and Affiliations

  1. UPE, ECOMP, Benfica St. 455, Recife, PE, 50720-001, Brazil

    Breno Santos, Bruna Aguiar & Mêuser Valença

Authors
  1. Breno Santos
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  2. Bruna Aguiar
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  3. Mêuser Valença
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Corresponding author

Correspondence to Breno Santos .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Santos, B., Aguiar, B., Valença, M. (2018). Applying Macroclimatic Variables to Improve Flow Rate Forecasting Using Neural Networks Techniques. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_6

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  • DOI: https://doi.org/10.1007/978-3-030-04221-9_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04220-2

  • Online ISBN: 978-3-030-04221-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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