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Estimation of Runoff Through BPNN and SVM in Agalpur Watershed

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Frontiers in Intelligent Computing: Theory and Applications

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

Abstract

Two different techniques support vector machine (SVM) and back propagation neural network (BPNN) employed to evaluate runoff for five proposed modeling inputs. Research is conducted at Agalpur watershed, Odisha, India. NSE, RMSE, and WI indicators are used for evaluation of performance of the model. Productivity of this work will propose development, plan, and administration of water-bound structures for mounting watershed. Presentation of this examiner is contrasted, and mapping is done with WI value. In BPNN, three different transfer functions like Tansig, Logsig, and Purelin are used to examine the model. Outcomes suggest that assessment of runoff is suitable to SVM in comparison to BPNN. Both BPNN and SVM perform well in complex data sets of projected watershed.

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Authors and Affiliations

  1. National Institute of Technology Silchar, 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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Cite this paper

Samantaray, S., Sahoo, A. (2020). Estimation of Runoff Through BPNN and SVM in Agalpur Watershed. 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_27

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