Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-457
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
31 Jul 2017
Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).
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 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.

Citation: Zhou, Y., Chang, F.-J., Guo, S., Ba, H., and He, S.: A robust recurrent ANFIS for modeling multi-step-ahead flood forecast of Three Gorges Reservoir in the Yangtze River, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-457, in review, 2017.
Yanlai Zhou et al.
Yanlai Zhou et al.
Yanlai Zhou et al.

Viewed

Total article views: 471 (including HTML, PDF, and XML)

HTML PDF XML Total BibTeX EndNote
402 67 2 471 5 8

Views and downloads (calculated since 31 Jul 2017)

Cumulative views and downloads (calculated since 31 Jul 2017)

Viewed (geographical distribution)

Total article views: 471 (including HTML, PDF, and XML)

Thereof 462 with geography defined and 9 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 23 Nov 2017
Publications Copernicus
Download
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.
Developing a robust recurrent ANFIS for modeling multi-step-ahead flood forecast. Fusing the LSE...
Share