Civil Engineering / İnşaat Mühendisliği

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  • Book Part
    Citation - Scopus: 4
    Application of Fuzzy Logic in Water Resources Engineering
    (Elsevier, 2022) Tayfur, Gökmen
    This chapter introduces the fundamentals of fuzzy logic (FL), fuzzy sets, and fuzzy model components such as the fuzzification, the fuzzy rule base, the fuzzy inference engine, and the defuzzification. The processes of the fuzzy model components are presented by working on the examples from the water resources engineering application problems. This chapter also discusses the merits and the shortcomings of the fuzzy modeling. Hydrological processes have inherent source of uncertainty, for which the fuzzy set theory can be an effective solution tool. © 2023 Elsevier Inc. All rights reserved.
  • Book Part
    Citation - Scopus: 1
    Developments in Sediment Transport Modeling in Alluvial Channels
    (Elsevier, 2022) Tayfur, Gökmen
    This chapter discusses the developments in the mathematical modeling of sediment transport dynamics in alluvial channels. Starting with early experimental and empirical studies, it goes on to treating the processes in 1D, 2D, and 3D uniform sediment transport. Finally, it describes the treatment of the processes in 3D nonuniform sediment transport considering turbulence effects. While introducing the advancements in mathematical modeling of the dynamics, the chapter also discusses the outstanding issues like the treatment of the particle fall velocity, the particle velocity, and sediment transport rate function. © 2023 Elsevier Inc. All rights reserved.
  • 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.