Preprints
https://doi.org/10.5194/hess-2017-457
https://doi.org/10.5194/hess-2017-457
 
31 Jul 2017
31 Jul 2017
Status: this preprint was under review for the journal HESS but the revision was not accepted.

A robust recurrent ANFIS for modeling multi-step-ahead flood forecast of Three Gorges Reservoir in the Yangtze River

Yanlai Zhou1,2, Fi-John Chang1, Shenglian Guo2, Huanhuan Ba2, and Shaokun He2 Yanlai Zhou et al.
  • 1Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan, ROC
  • 2State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China

Abstract. Accurate and robust multi-step-ahead flood forecast during flood season is extremely crucial to reservoir flood control. A modified hybrid learning algorithm, which fuses the Least Square Estimator (LSE) with Genetic Algorithm (GA), is proposed for optimizing the parameters of recurrent ANFIS (R-ANFIS) model to overcome the instability and local minima problems as well as improve model’s generalization and robustness. A coherent set of evaluation criteria is used to fully explore the model's accuracy (MAE, RMSE, CC & CE) and robustness (reliability, vulnerability & resilience). Three types of ANFIS (i.e. Classic, Recurrent, and Modified Recurrent) models with their optimal input variables identified by the Gamma Test are utilized for modeling multi-step-ahead flood forecast of Three Gorges Reservoir in the Yangtze River, respectively. Taking the horizon t + 12 (three days ahead), for example, the comparison analysis between C-ANFIS and R-ANFIS indicates that the R-ANFIS model can largely improve the CE, CC, reliability and resilience by 38.09 %, 17.36 %, 28.30 % & 140.26 % as well as significantly reduce the MAE, RMSE, vulnerability by 68.03 %, 47.98 % & 13.32 %. The comparison analysis between R-ANFIS and MR-ANFIS shows that the MR-ANFIS model can further enhance the CE, CC, reliability and resilience by 2.04 %, 2.04 %, 5.05 %, and 3.61 %, respectively, as well as decrease the MAE, RMSE, vulnerability by 9.91 %, 13.79 %, and 9.92 %, respectively. Such results evidently promote data-driven model's generalization (accuracy & robustness) and leads to better decisions on real-time reservoir operation during flood season.

Yanlai Zhou et al.

 
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Status: closed
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Yanlai Zhou et al.

Yanlai Zhou et al.

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Short summary
Developing a robust recurrent ANFIS for modeling multi-step-ahead flood forecast. Fusing the LSE into GA for optimizing the parameters of recurrent ANFIS. Improving the robustness and generalization of recurrent ANFIS. An accurate and robust multi-step-ahead inflow forecast in the Three Gorges Reservoir will provide precious decision-making time for effectively managing contingencies and emergencies and greatly alleviating flood risk as well as loss of life and property.