Real-Time Forecast of Reservoir Inflow Hydrographs Incorporating Terrain and Monsoon Effects during Typhoon Invasion by Novel Intelligent Numerical-Statistic Impulse Techniques
Publication: Journal of Hydrologic Engineering
Volume 20, Issue 10
Abstract
This study develops an original methodology for forecasting real-time reservoir inflow hydrographs during typhoons, taking advantage of meteoro-hydrological methods such as analysis of typhoon hydrographs, numerical typhoon track forecasts, statistic typhoon central impulse-based quantitative precipitation forecasts model based on a real-time revised approach (TCI-RTQPF), real-time recurrent learning neural network (RTRLNN), and adaptive network-based fuzzy inference system (ANFIS). To derive the inflow hydrograph induced by interaction between typhoon rain bands, terrain, and monsoons, the inventive novel ensemble numerical-statistic impulse techniques are employed. The inflow during peak flow, inflection, and direct runoff ending (DRE) periods (impulse signal) are used for the deriving process. The hydrograph analysis is used to examine the mechanism between typhoon center location, wind field, precipitation, and the inflow hydrograph, and to establish the evaluation methods. Additionally, a novel total inflow forecast model is developed using image hashing and ANFIS for selecting optimal derived hydrograph. The experiment is conducted in the Tseng-Wen Reservoir basin, Taiwan. Results demonstrate that the wind field–based and moving dynamics–based approach of typhoon can effectively evaluate the time of peak flow, inflection point, and DRE incorporating terrain and monsoon effects. The effective functions for deriving impulse signal include blended polynomial, exponential, and power functions, and for deriving inflow hydrograph, multinomial Gaussian functions. Finally, the real-time experimental outcomes show that the proposed innovative practical methodology can accurately forecast the real-time reservoir inflow hydrograph that the average error of Typhoon Krosa is 7.81% within 32 h average forecasted lead time, and Typhoon Morakot, 9.78% within 79 h forecasted lead time.
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Acknowledgments
This research was partially supported by the National Science Council, Taiwan (Grant No. NSC100-2625-M-002-008). In addition, the authors are indebted to the reviewers for their valuable comments and suggestions.
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Journal of Hydrologic Engineering
Volume 20 • Issue 10 • October 2015
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© 2015 American Society of Civil Engineers.
History
Received: Mar 1, 2014
Accepted: Nov 6, 2014
Published online: Feb 25, 2015
Discussion open until: Jul 25, 2015
Published in print: Oct 1, 2015
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