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Application of Hybrid ANFIS-CSA Model in Suspended Sediment Load Prediction

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Advanced Computing and Intelligent Technologies

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 914))

Abstract

Accurate suspended sediment load (SSL) prediction and modelling in rivers play an essential part in designing engineering structures, environmental science and watershed management. Recently, data-driven models have been majorly employed for estimating SSL, and these models have generated effective outcomes. This study compares the prediction capability of adaptive neuro-fuzzy inference systems (ANFIS) and hybrid ANFIS-CSA (crow search algorithm) techniques for SSL prediction. For developing the models, monthly average SSL data of Brahmani river basin for 30 years were obtained. Reliability of applied techniques is evaluated based on statistical criteria, which include correlation coefficient (r), root-mean-square-error (RMSE) and Nash–Sutcliffe efficiency (NSE). Obtained results revealed that ANFIS-CSA gave highest value of R and NSE, whereas lowest RMSE value than conventional ANFIS model. Results showed that combining optimisation algorithm and AI model improved the capability of models for SSL prediction.

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Author information

Authors and Affiliations

  1. Department of Civil Engineering, NIT Silchar, Silchar, Assam, India

    Abinash Sahoo

  2. Department of Civil Engineering, NIT Raipur, Raipur, Chhattisgarh, India

    Nihar R. Mohanta

  3. Department of Civil Engineering, OUTR, Bhubaneswar, Odisha, India

    Sandeep Samantaray & Deba Prakash Satapathy

Authors
  1. Abinash Sahoo
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  2. Nihar R. Mohanta
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  3. Sandeep Samantaray
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  4. Deba Prakash Satapathy
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Corresponding author

Correspondence to Sandeep Samantaray .

Editor information

Editors and Affiliations

  1. Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India

    Rabindra Nath Shaw

  2. Regional Campus Manipur, Indira Gandhi National Tribal University, Imphal, Manipur, India

    Sanjoy Das

  3. Department of Computer Science, University of Milan, Milan, Italy

    Vincenzo Piuri

  4. Department of Information Engineering and Mathematics, University of Siena, Siena, Italy

    Monica Bianchini

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Sahoo, A., Mohanta, N.R., Samantaray, S., Satapathy, D.P. (2022). Application of Hybrid ANFIS-CSA Model in Suspended Sediment Load Prediction. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Electrical Engineering, vol 914. Springer, Singapore. https://doi.org/10.1007/978-981-19-2980-9_24

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  • DOI: https://doi.org/10.1007/978-981-19-2980-9_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-2979-3

  • Online ISBN: 978-981-19-2980-9

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