Real-Time Flood Hydrograph Predictions Using Rating Curve and Soft Computing Methods (ga, Ann)
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Date
Authors
Tayfur, Gökmen
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Volume Title
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Open Access Color
Green Open Access
No
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Publicly Funded
No
Abstract
This chapter introduces hydraulic and hydrologic flood routing methods in natural channels. It details hydrological flood routing methods of the Rating Curve and Muskingum. Based on the rating curve method (RCM), it presents real-time flood hydrograph predictions using the genetic algorithm (GA-based RCM) model. In addition, it presents how to make real-time flood hydrograph predictions using the artificial neural network (ANN). The chapter briefly introduces the basics of GA and details how to calibrate and validate the GA-based RCM model using measured real-time flood hydrographs. Similarly, after giving the basics of ANN, it shows how to train and test the ANN model using measured hydrographs. Real hydrograph simulations by the RCM, GA-based RCM, and ANN are presented, and merits of each model are discussed. © 2023 Elsevier Inc. All rights reserved.
Description
Keywords
Artificial neural network, Calibration, Flood routing, Genetic algorithm, Rating Curve, Real hydrograph, Validation
Fields of Science
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WoS Q
Scopus Q

OpenCitations Citation Count
2
Volume
Issue
Start Page
325
End Page
338
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Citations
CrossRef : 3
Scopus : 3
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Mendeley Readers : 6
SCOPUS™ Citations
3
checked on Apr 27, 2026
Page Views
235
checked on Apr 27, 2026
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