Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Review Citation - WoS: 25Citation - Scopus: 35Evolution of Floods: From Ancient Times To the Present Times (ca 7600 Bc To the Present) and the Future(MDPI, 2023) Angelakis, Andreas N.; Capodaglio, Andrea G.; Valipour, Mohammad; Krasilnikoff, Jens; Ahmed, Abdelkader T.; Mandi, Laila; Tzanakakis, Vasileios A.; Kumar, Rohitashw; Min, Zhang; Han, Mooyoung; Bashiru, Turay; Derkas, Nicholas; Baba, Alper; Bilgiç, EsraFloods are one of the most dangerous natural disasters, causing great destruction, damage, and even fatalities worldwide. Flooding is the phenomenon of a sudden increase or even slow increase in the volume of water in a river or stream bed as the result of several possible factors: heavy or very long precipitation, melting snowpack, strong winds over the water, unusually high tides, tsunamis, or the failure of dams, gages, detention basins, or other structures that hold back water. To gain a better understanding of flooding, it is necessary to examine evidence, search for ancient wisdom, and compare flood-management practices in different regions in a chronological perspective. This study reviews flood events caused by rising sea levels and erratic weather from ancient times to the present. In addition, this review contemplates concerns about future flood challenges and possible countermeasures. Thus, it presents a catalogue of past examples in order to present a point of departure for the study of ancient floods and to learn lessons for preparation for future flood incidents including heavy rainfalls, particularly in urbanized areas. The study results show that ancient societies developed multifaceted technologies to cope with floods and many of them are still usable now and may even represent solutions and measures to counter the changing and increasingly more erratic weather of the present.Article Citation - WoS: 27Citation - Scopus: 101Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Neural Network(American Society of Civil Engineers (ASCE), 2005) Tayfur, G; Singh, VPAn artificial neural network (ANN) model was developed to predict the longitudinal dispersion coefficient in natural streams and rivers. The hydraulic variables [flow discharge (Q), flow depth (H), flow velocity (U), shear velocity (u*), and relative shear velocity (U/u*)] and geometric characteristics [channel width (B), channel sinuosity (sigma), and channel shape parameter (beta)] constituted inputs to the ANN model, whereas the dispersion coefficient (K-x) was the target model output. The model was trained and tested using 71 data sets of hydraulic and geometric parameters and dispersion coefficients measured on 29 streams and rivers in the United States. The training of the ANN model was accomplished with an explained variance of 90% of the dispersion coefficient. The dispersion coefficient values predicted by the ANN model satisfactorily compared with the measured values corresponding to different hydraulic and geometric characteristics. The predicted values were also compared with those predicted using several equations that have been suggested in the literature and it was found that the ANN model was superior in predicting the dispersion coefficient. The results of sensitivity analysis indicated that the Q data alone would be sufficient for predicting more frequently occurring low values of the dispersion coefficient (K-x < 100 m(2)/s). For narrower channels (B/H < 50) using only U/u* data would be sufficient to predict the coefficient. If beta and sigma were used along with the flow variables, the prediction capability of the ANN model would be significantly improved.