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Frontiers in Intelligent Computing: Theory and Applications pp 258–267Cite as

Appraisal of Runoff Through BPNN, RNN, and RBFN in Tentulikhunti Watershed: A Case Study

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1014))

Abstract

Three unlike neural network models, (i) radial basis fewer network (RBFN) model, (ii) recurrent neural network (RNN), and (iii) backpropagation neural network (BPNN) model, are employed to guesstimate runoff at Tentulikhunti watershed, Odisha, India. Scenarios with minimum temperature, maximum temperature, and precipitation are considered for experiencing the impact on runoff. In Tentulikhunti watershed, RNN executes preeminent by means of architecture 4-3-1 succeeding tangential sigmoid transfer function. Equally, RBFN and BPNN perform in parallel with small deviation of prediction for predicting runoff.

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Author information

Authors and Affiliations

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

    Sandeep Samantaray & Abinash Sahoo

Authors
  1. Sandeep Samantaray
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  2. Abinash Sahoo
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Corresponding author

Correspondence to Sandeep Samantaray .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Electronics and Communication Engineering, SRMGPC, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Duy Tan University, Da Nang, Vietnam

    Bao Le Nguyen

  4. Graduate School, Duy Tan University, Da Nang, Vietnam

    Nhu Gia Nguyen

  5. Faculty of Information Technology, Hai Phong University, Hai Phong, Vietnam

    Dac-Nhuong Le

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Samantaray, S., Sahoo, A. (2020). Appraisal of Runoff Through BPNN, RNN, and RBFN in Tentulikhunti Watershed: A Case Study. In: Satapathy, S., Bhateja, V., Nguyen, B., Nguyen, N., Le, DN. (eds) Frontiers in Intelligent Computing: Theory and Applications. Advances in Intelligent Systems and Computing, vol 1014. Springer, Singapore. https://doi.org/10.1007/978-981-13-9920-6_26

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