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Input Selection of Wavelet-Coupled Neural Network Models for Rainfall-Runoff Modelling

Author

Listed:
  • Muhammad Shoaib

    (Bahauddin Zakariaya University
    The University of Auckland)

  • Asaad Y. Shamseldin

    (The University of Auckland)

  • Sher Khan

    (The University of Auckland)

  • Muhammad Sultan

    (Bahauddin Zakariaya University)

  • Fiaz Ahmad

    (Bahauddin Zakariaya University)

  • Tahir Sultan

    (Bahauddin Zakariaya University)

  • Zakir Hussain Dahri

    (Wageningen University & Research
    Pakistan Agricultural Research Council)

  • Irfan Ali

    (Pakistan Agricultural Research Council)

Abstract

The use of wavelet-coupled data-driven models is increasing in the field of hydrological modelling. However, wavelet-coupled artificial neural network (ANN) models inherit the disadvantages of containing more complex structure and enhanced simulation time as a result of use of increased multiple input sub-series obtained by the wavelet transformation (WT). So, the identification of dominant wavelet sub-series containing significant information regarding the hydrological system and subsequent use of those dominant sub-series only as input is crucial for the development of wavelet-coupled ANN models. This study is therefore conducted to evaluate various approaches for selection of dominant wavelet sub-series and their effect on other critical issues of suitable wavelet function, decomposition level and input vector for the development of wavelet-coupled rainfall-runoff models. Four different approaches to identify dominant wavelet sub-series, ten different wavelet functions, nine decomposition levels, and five different input vectors are considered in the present study. Out of four tested approaches, the study advocates the use of relative weight analysis (RWA) for the selection of dominant input wavelet sub-series in the development of wavelet-coupled models. The db8 and the dmey (Discrete approximation of Meyer) wavelet functions at level nine were found to provide the best performance with the RWA approach.

Suggested Citation

  • Muhammad Shoaib & Asaad Y. Shamseldin & Sher Khan & Muhammad Sultan & Fiaz Ahmad & Tahir Sultan & Zakir Hussain Dahri & Irfan Ali, 2019. "Input Selection of Wavelet-Coupled Neural Network Models for Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 955-973, February.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:3:d:10.1007_s11269-018-2151-x
    DOI: 10.1007/s11269-018-2151-x
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    References listed on IDEAS

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    1. Ozgur Kisi, 2011. "Wavelet Regression Model as an Alternative to Neural Networks for River Stage Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 579-600, January.
    2. Muhammad Shoaib & Asaad Y. Shamseldin & Sher Khan & Mudasser Muneer Khan & Zahid Mahmood Khan & Tahir Sultan & Bruce W. Melville, 2018. "A Comparative Study of Various Hybrid Wavelet Feedforward Neural Network Models for Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 83-103, January.
    3. Ozgur Kisi & Jalal Shiri, 2011. "Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(13), pages 3135-3152, October.
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    Cited by:

    1. Nourani, Vahid & Sharghi, Elnaz & Behfar, Nazanin & Zhang, Yongqiang, 2022. "Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data," Applied Energy, Elsevier, vol. 315(C).
    2. Babak Vaheddoost & Hafzullah Aksoy, 2019. "Reconstruction of Hydrometeorological Data in Lake Urmia Basin by Frequency Domain Analysis Using Additive Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3899-3911, September.
    3. José-Luis Molina & Santiago Zazo & Ana-María Martín-Casado & María-Carmen Patino-Alonso, 2020. "Rivers’ Temporal Sustainability through the Evaluation of Predictive Runoff Methods," Sustainability, MDPI, vol. 12(5), pages 1-21, February.

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