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Modeling Runoff Using Feed Forward-Back Propagation and Layer Recurrent Neural Networks

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Proceedings of the 2nd International Conference on Data Engineering and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 828))

Abstract

This work describes the function of (i) feed forward-back propagation network (FFBPN) model and (ii) layer recurrent network (LRN), to predict runoff as a function of rainfall, temperature, and evapotranspiration loss. For model architecture, the criteria for evaluation are mean square error training, testing, root mean square error training, testing, and coefficient of determination. Overall results found that LRN performs best as compared to FFBPN for predicting runoff in the watershed. This result will help for planning, design, and management of hydraulic structures in the vicinity of the watershed.

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Author information

Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology, Silchar, Assam, India

    Dillip Kumar Ghose

Authors
  1. Dillip Kumar Ghose
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Correspondence to Dillip Kumar Ghose .

Editor information

Editors and Affiliations

  1. Symbiosis Institute of Technology, Symbiosis International University, Pune, Maharashtra, India

    Anand J. Kulkarni

  2. Department of CSE, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  3. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Tai Kang

  4. Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

    Ali Husseinzadeh Kashan

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Ghose, D.K. (2019). Modeling Runoff Using Feed Forward-Back Propagation and Layer Recurrent Neural Networks. In: Kulkarni, A., Satapathy, S., Kang, T., Kashan, A. (eds) Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-13-1610-4_8

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  • DOI: https://doi.org/10.1007/978-981-13-1610-4_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1609-8

  • Online ISBN: 978-981-13-1610-4

  • eBook Packages: EngineeringEngineering (R0)

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