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Discussion papers | Copyright
https://doi.org/10.5194/hess-2017-457
© Author(s) 2017. This work is distributed under
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Research article 31 Jul 2017

Research article | 31 Jul 2017

Review status
This discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The revised manuscript 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.

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Yanlai Zhou et al.
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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.
Developing a robust recurrent ANFIS for modeling multi-step-ahead flood forecast. Fusing the LSE...
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