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Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach

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Abstract

Accurate water level forecasting is important to understand and provide an early warning of flood risk and discharge. It is also crucial for many plants and animal species that needs specific ranges of water level. This research focused on long term multi-step ahead forecasting of daily flood water level in duration of (2005–2021) at two stations (i.e., Baryulgil and Lilydale) of the Clarence River, in Australia, introducing a novel hybrid framework coupling time varying filter-based empirical mode decomposition (TVF-EMD), classification and regression trees (CART) feature selection, and four advanced machine learning (ML) models. The implemented ML approaches are including Long-Short Term Memory (LSTM), cascaded forward neural network (CFNN), gradient boosting decision tree (GBDT), and multivariate adaptive regression spline (MARS). Here, original time series of WL in each station was decomposed into the optimal intrinsic mode functions (IMFs) using the TVF-EMD technique and the significant lagged-time components for two desired horizons (t + 1 and t + 7 time ahead) in each station was extracted by using the CART-feature selection method. Then, the IMFs and corresponded residual obtained from the pre-processing procedure were separately implemented to feed the ML models and produce the CART-TVF-EMD-LSTM, CART-TVF-EMD-CFNN, CART-TVF-EMD-MARS, and CART-TVF-EMD-GBDT by assembling all the individual sub-sequences outcomes. Several goodness-of-fit metrics such as correlation coefficient (R), Mean absolute percentage error (MAPE), and Kling-Gupta efficiency (KGE) and the infographic tools and diagnostic analysis were employed to evaluate the robustness of the provided techniques. The outcomes of developed expert systems ascertained that CART-TVF-EMD-CFNN for one- and seven-day horizons in both stations outperformed the CART-TVF-EMD-MARS, CART-TVF-EMD-LSTM, CART-TVF-EMD-GBDT, and all the standalone counterpart models (i.e., CFNN, MARS, LSTM, and GBDT) respectively. As one of the most important achievements of this research, the LSTM did not lead to superior and promising results in the long-term highly nonstationary time series.

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Acknowledgements

The authors are thankful to the Bureau of Meteorology, Australia and the NSW Office of Water and the NSW Department of Service Technology and Administration (Manly Hydraulics Laboratory).

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Authors declare that there is no funding source available.

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Authors and Affiliations

  1. Faculty of Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Dashte Azadegan, Iran

    Mehdi Jamei

  2. School of Information Technology, Deakin University, Burwood, VIC, 3125, Australia

    Mumtaz Ali

  3. Punjab Agricultural University, Regional Research Station, Punjab, Bathinda, 151001, India

    Anurag Malik

  4. Department of Science, School of Science and Technology, The University of Fiji, Lautoka, Fiji

    Ramendra Prasad

  5. USQ College, University of Southern Queensland, Toowoomba, Australia

    Mumtaz Ali & Shahab Abdulla

  6. Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Zaher Mundher Yaseen

  7. New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq

    Zaher Mundher Yaseen

  8. Adjunct Research Fellow, USQ’s Advanced Data Analytics Research Group, School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, 4350, Australia

    Zaher Mundher Yaseen

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  1. Mehdi Jamei
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  4. Ramendra Prasad
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  5. Shahab Abdulla
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Contributions

Mehdi Jamie: Writing- Visualization, Original draft preparation, Conceptualization, Methodology, and Software. Mumtaz Ali: Original draft preparation, Writing- Reviewing and Editing, Investigation, Supervision. Anurag Malik: Writing- Reviewing and Editing. Ramendra Prasad: Writing- Reviewing and Editing. Shahab Abdulla: Reviewing and Editing. Zaher Mundher Yaseen: Supervision, writing manuscript, visualizing, validation, investigation, reviewing and editing.

Corresponding authors

Correspondence to Mehdi Jamei or Mumtaz Ali.

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Jamei, M., Ali, M., Malik, A. et al. Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach. Water Resour Manage 36, 4637–4676 (2022). https://doi.org/10.1007/s11269-022-03270-6

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