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Online Ensemble Modeling for Real Time Water Level Forecasts

Author

Listed:
  • Lan Yu

    (Nanyang Technological University)

  • Soon Keat Tan

    (Nanyang Technological University
    Nanyang Environment and Water Research Institute (NEWRI))

  • Lloyd H. C. Chua

    (Deakin University)

Abstract

Accurate and reliable flood forecasting is essential to mitigate the threats brought by floods. Ensemble approaches have been used in limited studies to improve the forecasts of component models. In this paper an ensemble model based on neural-fuzzy inference system (NFIS) and three real time updating approaches were used to synthesize the water level forecasts from a Adaptive-Network-based Fuzzy Inference System (ANFIS) model and the Unified River Basin Simulator (URBS) model for three stations in Lower Mekong. The NFIS ensemble model results are compared with the simple average model (SAM) which is adopted as a benchmark ensemble model. The ensemble model of offline learning without real time updating (EN-OFF), ensemble model with real time updating using offline learning (EN-RTOFF), ensemble model with real time updating using online learning (EN-RTON1) and ensemble model with real time updating using online learning and sub-models (EN-RTON2) were studied in this paper. Statistical analysis of the models for all the three stations indicated the superiority of the EN-RTON2 model over EN-RTOFF, EN-RTON1 models, SAM and the EN-OFF model. Not only the spikes in the URBS model were eliminated, but also the time shift problems in the ANFIS model results were decreased.

Suggested Citation

  • Lan Yu & Soon Keat Tan & Lloyd H. C. Chua, 2017. "Online Ensemble Modeling for Real Time Water Level Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1105-1119, March.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:4:d:10.1007_s11269-016-1539-8
    DOI: 10.1007/s11269-016-1539-8
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    References listed on IDEAS

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    1. Seyed Ahmad Soleymani & Shidrokh Goudarzi & Mohammad Hossein Anisi & Wan Haslina Hassan & Mohd Yamani Idna Idris & Shahaboddin Shamshirband & Noorzaily Mohamed Noor & Ismail Ahmedy, 2016. "A Novel Method to Water Level Prediction using RBF and FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3265-3283, July.
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    3. Habib Akbari-Alashti & Omid Bozorg Haddad & Miguel Mariño, 2015. "Evaluation of a Developed Discrete Time-Series Method in Flow Forecasting Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3211-3225, July.
    4. Pao-Shan Yu & Tao-Chang Yang, 1997. "A Probability-Based Renewal Rainfall Model for Flow Forecasting," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 15(1), pages 51-70, January.
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    Cited by:

    1. Hua’an Wu & Bo Zeng & Meng Zhou, 2017. "Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption," IJERPH, MDPI, vol. 14(11), pages 1-12, November.
    2. José P. Matos & Maria M. Portela & Anton J. Schleiss, 2018. "Towards Safer Data-Driven Forecasting of Extreme Streamflows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 701-720, January.

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