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

Author

Listed:
  • Mehdi Jamei

    (Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz)

  • Mumtaz Ali

    (Deakin University
    USQ College, University of Southern Queensland)

  • Anurag Malik

    (Regional Research Station)

  • Ramendra Prasad

    (The University of Fiji)

  • Shahab Abdulla

    (USQ College, University of Southern Queensland)

  • Zaher Mundher Yaseen

    (Universiti Kebangsaan Malaysia
    Al-Ayen University
    University of Southern Queensland)

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.

Suggested Citation

  • Mehdi Jamei & Mumtaz Ali & Anurag Malik & Ramendra Prasad & Shahab Abdulla & Zaher Mundher Yaseen, 2022. "Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4637-4676, September.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:12:d:10.1007_s11269-022-03270-6
    DOI: 10.1007/s11269-022-03270-6
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    References listed on IDEAS

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    Cited by:

    1. Xi Yang & Zhihe Chen & Min Qin, 2024. "Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 269-286, January.
    2. Jamei, Mehdi & Sharma, Prabhakar & Ali, Mumtaz & Bora, Bhaskor J. & Malik, Anurag & Paramasivam, Prabhu & Farooque, Aitazaz A. & Abdulla, Shahab, 2024. "Application of an explainable glass-box machine learning approach for prognostic analysis of a biogas-powered small agriculture engine," Energy, Elsevier, vol. 288(C).
    3. Jingwei Huang & Hui Qin & Yongchuan Zhang & Dongkai Hou & Sipeng Zhu & Pingan Ren, 2023. "Short-term Prediction Method of Reservoir Downstream Water Level Under Complicated Hydraulic Influence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4475-4490, September.
    4. Mohammad Ehtearm & Hossein Ghayoumi Zadeh & Akram Seifi & Ali Fayazi & Majid Dehghani, 2023. "Predicting Hydropower Production Using Deep Learning CNN-ANN Hybridized with Gaussian Process Regression and Salp Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3671-3697, July.

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