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Prediction of Suspended Sediment Load Using Radial Basis Neural Network

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Intelligent Engineering Informatics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 695))

Abstract

Prediction of suspended sediment is vital for soil erosion during peak periods of inundation. The present work focused on development of modeling for suspended sediment concentration via Radial Basis Neural Network (RBNN) models. Parameters such as rainfall, discharge have been collected on daily basis from Rajghat gauging station of Subarnarekha basin to develop the model. Different architectures of models are fixed to envisage the performance of models during July, August, and September of monsoon period for measuring suspended sediment load. The individual best performances for different models are found out to measure sediment load during peak period of monsoon. Among July, August, and September, the model performance says the highest potential of erosion occurs during the month of September from the nearest zone of watersheds to the river basin during the peak period of flood. This work brings an idea for indirect measurement of sediment delivery ratio.

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Author information

Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology, Silchar, 788010, Assam, India

    Dillip K. Ghose

Authors
  1. Dillip K. Ghose
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Correspondence to Dillip K. Ghose .

Editor information

Editors and Affiliations

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

    Vikrant Bhateja

  2. Departamento de Computación, CINVESTAV-IPN, Mexico City, Mexico

    Carlos A. Coello Coello

  3. Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  4. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

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Ghose, D.K. (2018). Prediction of Suspended Sediment Load Using Radial Basis Neural Network. In: Bhateja, V., Coello Coello, C., Satapathy, S., Pattnaik, P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-10-7566-7_60

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  • DOI: https://doi.org/10.1007/978-981-10-7566-7_60

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7565-0

  • Online ISBN: 978-981-10-7566-7

  • eBook Packages: EngineeringEngineering (R0)

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