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Comparative Study of Three Updating Procedures for Real-Time Flood Forecasting

Author

Listed:
  • Zhangjun Liu

    (Wuhan University)

  • Shenglian Guo

    (Wuhan University)

  • Honggang Zhang

    (Changjiang Water Resources Commission)

  • Dedi Liu

    (Wuhan University)

  • Guang Yang

    (Wuhan University)

Abstract

Accurate real-time flood forecasting is essential for flood control and warning system, reservoir operation and other relevant water resources management activities. The objective of this study is to investigate and compare the capability of three updating procedures, namely autoregressive (AR) model, recursive least-squares (RLS) model and hydrologic uncertainty processor (HUP) in the real-time flood forecasting. The Baiyunshan reservoir basin located in southern China was selected as a case study. These three procedures were employed to update outputs of the established Xinanjiang flood forecasting model. The Nash-Sutcliffe efficiency (NSE) and Relative Error (RE) are used as model evaluation criteria. It is found that all of these three updating procedures significantly improve the accuracy of Xinanjiang model when operating in real-time forecasting mode. Comparison results also indicated that the HUP performed better than the AR and RLS models, while RLS model was slightly superior to AR model. In addition, the HUP implemented in the probabilistic form can quantify the uncertainty of the actual discharge to be forecasted and provide a posterior distribution as well as interval estimation, which offer more useful information than two other deterministic updating procedures. Thus, the HUP updating procedure is more promising and recommended for real-time flood forecasting in practice.

Suggested Citation

  • Zhangjun Liu & Shenglian Guo & Honggang Zhang & Dedi Liu & Guang Yang, 2016. "Comparative Study of Three Updating Procedures for Real-Time Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2111-2126, May.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:7:d:10.1007_s11269-016-1275-0
    DOI: 10.1007/s11269-016-1275-0
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    1. Muhammad Aqil & Ichiro Kita & Akira Yano & Soichi Nishiyama, 2007. "Neural Networks for Real Time Catchment Flow Modeling and Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(10), pages 1781-1796, October.
    2. S. Jonkman, 2005. "Global Perspectives on Loss of Human Life Caused by Floods," 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. 34(2), pages 151-175, February.
    3. Jiang Wu & Jianzhong Zhou & Lu Chen & Lei Ye, 2015. "Coupling Forecast Methods of Multiple Rainfall–Runoff Models for Improving the Precision of Hydrological Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5091-5108, November.
    4. 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.
    5. Sanjeet Kumar & Mukesh Tiwari & Chandranath Chatterjee & Ashok Mishra, 2015. "Reservoir Inflow Forecasting Using Ensemble Models Based on Neural Networks, Wavelet Analysis and Bootstrap Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4863-4883, October.
    6. M. Akbari & A. Afshar & M. Sadrabadi, 2009. "Fuzzy Rule Based Models Modification by New Data: Application to Flood Flow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(12), pages 2491-2504, September.
    7. Benedetto Calvo & Fabrizio Savi, 2009. "Real-time flood forecasting of the Tiber river in Rome," 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. 50(3), pages 461-477, September.
    8. Aman Mohammad Kalteh, 2016. "Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 747-766, January.
    9. Taymoor Awchi, 2014. "River Discharges Forecasting In Northern Iraq Using Different ANN Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 801-814, February.
    10. Shivshanker Patel & Parthasarathy Ramachandran, 2015. "A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 589-602, January.
    11. Aman Kalteh, 2016. "Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 747-766, January.
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    3. Isabel Kaufmann Almeida & Aleska Kaufmann Almeida & Jorge Luiz Steffen & Teodorico Alves Sobrinho, 2016. "Model for Estimating the Time of Concentration in Watersheds," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4083-4096, September.
    4. Jiazheng Lu & Jun Guo & Li Yang & Xunjian Xu, 2017. "Research of reservoir watershed fine zoning and flood forecasting method," 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. 89(3), pages 1291-1306, December.
    5. Shenlin Li & Xiaohong Chen & Vijay P. Singh & Yanhu He, 2018. "Assumption-Simulation-Feedback-Adjustment (ASFA) Framework for Real-Time Correction of Water Resources Allocation: a Case Study of Longgang River Basin in Southern China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(12), pages 3871-3886, September.
    6. Zhangjun Liu & Jingwen Zhang & Tianfu Wen & Jingqing Cheng, 2022. "Uncertainty Quantification of Rainfall-runoff Simulations Using the Copula-based Bayesian Processor: Impacts of Seasonality, Copula Selection and Correlation Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 4981-4993, October.
    7. Adam P. Piotrowski & Jaroslaw J. Napiorkowski & Marzena Osuch, 2019. "Relationship Between Calibration Time and Final Performance of Conceptual Rainfall-Runoff Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 19-37, January.
    8. Sheng Sheng & Hua Chen & Fu-Qiang Guo & Jie Chen & Chong-Yu Xu & Sheng-lian Guo, 2020. "Transferability of a Conceptual Hydrological Model across Different Temporal Scales and Basin Sizes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2953-2968, July.

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