IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i12d10.1007_s11269-022-03270-6.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03270-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-022-03270-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fang‐Fang Li & Zhi‐Yu Wang & Jun Qiu, 2019. "Long‐term streamflow forecasting using artificial neural network based on preprocessing technique," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 192-206, April.
    2. Alizamir, Meysam & Kim, Sungwon & Kisi, Ozgur & Zounemat-Kermani, Mohammad, 2020. "A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions," Energy, Elsevier, vol. 197(C).
    3. Sloughter, J. McLean & Gneiting, Tilmann & Raftery, Adrian E., 2010. "Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 25-35.
    4. M. A. Ghorbani & R. Khatibi & V. Karimi & Zaher Mundher Yaseen & M. Zounemat-Kermani, 2018. "Learning from Multiple Models Using Artificial Intelligence to Improve Model Prediction Accuracies: Application to River Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4201-4215, October.
    5. Yin, Juan & Deng, Zhen & Ines, Amor V.M. & Wu, Junbin & Rasu, Eeswaran, 2020. "Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)," Agricultural Water Management, Elsevier, vol. 242(C).
    6. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    7. Prasad, Ramendra & Ali, Mumtaz & Xiang, Yong & Khan, Huma, 2020. "A double decomposition-based modelling approach to forecast weekly solar radiation," Renewable Energy, Elsevier, vol. 152(C), pages 9-22.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Adisa Hammed Akinsoji & Bashir Adelodun & Qudus Adeyi & Rahmon Abiodun Salau & Golden Odey & Kyung Sook Choi, 2024. "Integrating Machine Learning Models with Comprehensive Data Strategies and Optimization Techniques to Enhance Flood Prediction Accuracy: A Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4735-4761, September.
    4. 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.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Karbasi, Masoud & Jamei, Mehdi & Ali, Mumtaz & Malik, Anurag & Chu, Xuefeng & Farooque, Aitazaz Ahsan & Yaseen, Zaher Mundher, 2023. "Development of an enhanced bidirectional recurrent neural network combined with time-varying filter-based empirical mode decomposition to forecast weekly reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 290(C).
    2. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    3. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    4. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    5. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2010. "Combining predictive densities using Bayesian filtering with applications to US economics data," Working Paper 2010/29, Norges Bank.
    6. Nahushananda Chakravarthy H G & Karthik M Seenappa & Sujay Raghavendra Naganna & Dayananda Pruthviraja, 2023. "Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    7. Tim Voigt & Martin Kohlhase & Oliver Nelles, 2021. "Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    8. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    9. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
    10. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    11. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    12. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    13. Smyl, Slawek & Hua, N. Grace, 2019. "Machine learning methods for GEFCom2017 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1424-1431.
    14. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
    15. Eike Emrich & Christian Pierdzioch, 2016. "Volunteering, Match Quality, and Internet Use," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 136(2), pages 199-226.
    16. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    17. Zhu, Siying & Zhu, Feng, 2019. "Cycling comfort evaluation with instrumented probe bicycle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 217-231.
    18. Zhao, Jing & Guo, Zhen-Hai & Su, Zhong-Yue & Zhao, Zhi-Yuan & Xiao, Xia & Liu, Feng, 2016. "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, Elsevier, vol. 162(C), pages 808-826.
    19. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    20. Zhao, He & Huang, Xiaoqiao & Xiao, Zenan & Shi, Haoyuan & Li, Chengli & Tai, Yonghang, 2024. "Week-ahead hourly solar irradiation forecasting method based on ICEEMDAN and TimesNet networks," Renewable Energy, Elsevier, vol. 220(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:36:y:2022:i:12:d:10.1007_s11269-022-03270-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.