Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Access type: Metadata onlySubject: search.filters.subject.Artificial neural network

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  • Book Part
    Citation - Scopus: 3
    Real-Time Flood Hydrograph Predictions Using Rating Curve and Soft Computing Methods (ga, Ann)
    (Elsevier, 2022) Tayfur, Gökmen
    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.
  • Conference Object
    Citation - Scopus: 1
    Modelling Twin Rotor System With Artificial Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2015) Deniz, Meryem; Bıdıklı, Barış; Bayrak, Alper; Özdemirel, Barbaros; Tatlıcıoğlu, Enver
    In this study, the input output relation of the twin rotor system which was constructed in our laboratory is obtained by using ANNs. When compared with the existing literature, main advantage of this modelling approach is that multi input multi output ANN structure is used preferred. As a result of this approach, the cross coupling effects, between the rotors and also between the outputs, are taken into consideration. Thus, we sincerely believe that the obtained input output model demonstrates a close behavior to the real system.