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Reservoir Inflow Prediction by Ensembling Wavelet and Bootstrap Techniques to Multiple Linear Regression Model

Author

Listed:
  • Adnan Bashir

    (Bahauddin Zakaryia University)

  • Muhammad Ahmed Shehzad

    (Bahauddin Zakaryia University)

  • Ijaz Hussain

    (Quaid-e-Azam University)

  • Muhammad Ishaq Asif Rehmani

    (Ghazi University)

  • Sajjad Haider Bhatti

    (Governement College University)

Abstract

In this study, a new hybrid model, bootstrap multiple linear regression (BMLR) is suggested to investigate the potential of bootstrap resampling technique for daily reservoir inflow prediction. The proposed model compares with three other models: Multiple linear regression (MLR), wavelet multiple linear regression (WMLR) and wavelet bootstrap multiple linear regression (WBMLR). River stage data of monsoon season (1st July 2010 to 30 September 2010) from three gauging stations of Chenab river basin are used. In wavelet transformation, input vectors are decomposed using discrete wavelet transformation (DWT) into discrete wavelet components (DWCs). Then suitable DWCs are used to provide input to MLR model to develop WMLR model. Bootstrap technique coupled with MLR model to build up BMLR model. While WBMLR model is the conjunction of suitable DWCs and bootstrap technique to MLR model. Performance indices namely root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe coefficient of efficiency (NSC), and persistence index (CP) are used in study to evaluate the performance of model. Results showed that hybrid model BMLR produce significantly better results on performance indices than other models MLR, WMLR and WBMLR.

Suggested Citation

  • Adnan Bashir & Muhammad Ahmed Shehzad & Ijaz Hussain & Muhammad Ishaq Asif Rehmani & Sajjad Haider Bhatti, 2019. "Reservoir Inflow Prediction by Ensembling Wavelet and Bootstrap Techniques to Multiple Linear Regression Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5121-5136, December.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:15:d:10.1007_s11269-019-02418-1
    DOI: 10.1007/s11269-019-02418-1
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    References listed on IDEAS

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    1. Rajeev Sahay & Ayush Srivastava, 2014. "Predicting Monsoon Floods in Rivers Embedding Wavelet Transform, Genetic Algorithm and Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 301-317, January.
    2. Zaw Latt & Hartmut Wittenberg, 2014. "Improving Flood Forecasting in a Developing Country: A Comparative Study of Stepwise Multiple Linear Regression and Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2109-2128, June.
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    Cited by:

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    2. Haibo Chu & Jianmin Bian & Qi Lang & Xiaoqing Sun & Zhuoqi Wang, 2022. "Daily Groundwater Level Prediction and Uncertainty Using LSTM Coupled with PMI and Bootstrap Incorporating Teleconnection Patterns Information," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
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    5. Jinping Zhang & Honglin Xiao & Hongyuan Fang, 2022. "Component-based Reconstruction Prediction of Runoff at Multi-time Scales in the Source Area of the Yellow River Based on the ARMA Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 433-448, January.

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