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A Comparative Study of Various Hybrid Wavelet Feedforward Neural Network Models for Runoff Forecasting

Author

Listed:
  • Muhammad Shoaib

    (Bahauddin Zakariya University)

  • Asaad Y. Shamseldin

    (The University of Auckland)

  • Sher Khan

    (The University of Auckland)

  • Mudasser Muneer Khan

    (Bahauddin Zakariya University)

  • Zahid Mahmood Khan

    (Bahauddin Zakariya University)

  • Tahir Sultan

    (Bahauddin Zakariya University)

  • Bruce W. Melville

    (The University of Auckland)

Abstract

Considering network topologies and structures of the artificial neural network (ANN) used in the field of hydrology, one can categorize them into two different generic types: feedforward and feedback (recurrent) networks. Different types of feedforward and recurrent ANNs are available, but multilayer perceptron type of feedforward ANN is most commonly used in hydrology for the development of wavelet coupled neural network (WNN) models. This study is conducted to compare performance of the various wavelet based feedforward artificial neural network (ANN) models. The feedforward ANN types used in the study include the multilayer perceptron neural network (MLPNN), generalized feedforward neural network (GFFNN), radial basis function neural network (RBFNN), modular neural network (MNN) and neuro-fuzzy neural network (NFNN) models. The rainfall-runoff data of four catchments located in different hydro-climatic regions of the world is used in the study. The discrete wavelet transformation (DWT) is used in the present study to decompose input rainfall data using db8 wavelet function. A total of 220 models are developed in this study to evaluate the performance of various feedforward neural network models. Performance of the developed WNN models is compared with their counterpart simple models developed without applying wavelet transformation (WT). The results of the study are further compared with - multiple linear regression (MLR) model which suggest that the WNN models outperformed their counterpart simple models. The hybrid wavelet models developed using MLPNN, the GFFNN and the MNN models performed best among the six selected data driven models explored in the study. Moreover, performance of the three best models is found to be similar and thus the hybrid wavelet GFFNN and the MNN models can be considered as an alternative to the most commonly used hybrid WNN models developed using MLPNN. The study further reveals that the wavelet coupled models outperformed their counterpart simple models only with the parsimonious input vector.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:1:d:10.1007_s11269-017-1796-1
    DOI: 10.1007/s11269-017-1796-1
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    References listed on IDEAS

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    1. Wensheng Wang & Juliang Jin & Yueqing Li, 2009. "Prediction of Inflow at Three Gorges Dam in Yangtze River with Wavelet Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(13), pages 2791-2803, October.
    2. Hirad Abghari & Hojjat Ahmadi & Sina Besharat & Vahid Rezaverdinejad, 2012. "Prediction of Daily Pan Evaporation using Wavelet Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(12), pages 3639-3652, September.
    3. Nason, G.P. & von Sachs, R., 1999. "Wavelets in Time Series Analysis," Papers 9901, Catholique de Louvain - Institut de statistique.
    4. Vahid Moosavi & Mehdi Vafakhah & Bagher Shirmohammadi & Negin Behnia, 2013. "A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1301-1321, March.
    5. Sanjeet Kumar & Mukesh Tiwari & Chandranath Chatterjee & Ashok Mishra, 2015. "Reservoir Inflow Forecasting Using Ensemble Models Based on Neural Networks, Wavelet Analysis and Bootstrap Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4863-4883, October.
    6. Rob J. Hyndman & Andrey V. Kostenko, 2007. "Minimum Sample Size requirements for Seasonal Forecasting Models," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 12-15, Spring.
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    Cited by:

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    2. Elbeltagi, Ahmed & Srivastava, Aman & Deng, Jinsong & Li, Zhibin & Raza, Ali & Khadke, Leena & Yu, Zhoulu & El-Rawy, Mustafa, 2023. "Forecasting vapor pressure deficit for agricultural water management using machine learning in semi-arid environments," Agricultural Water Management, Elsevier, vol. 283(C).
    3. Tarate Suryakant Bajirao & Pravendra Kumar & Manish Kumar & Ahmed Elbeltagi & Alban Kuriqi, 2021. "Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers," Sustainability, MDPI, vol. 13(2), pages 1-29, January.
    4. Zhengfang Jiang & Baohong Lu & Zunguang Zhou & Yirui Zhao, 2024. "Comparison of Process-Driven SWAT Model and Data-Driven Machine Learning Techniques in Simulating Streamflow: A Case Study in the Fenhe River Basin," Sustainability, MDPI, vol. 16(14), pages 1-21, July.
    5. Shengli Liao & Huan Wang & Benxi Liu & Xiangyu Ma & Binbin Zhou & Huaying Su, 2023. "Runoff Forecast Model Based on an EEMD-ANN and Meteorological Factors Using a Multicore Parallel Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1539-1555, March.
    6. 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.

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