Wind energy gains more attention day by day as one of the clean renewable energy resources. We predicted wind speed vertical extrapolation by using extended power law. In this study, an extended vertical wind velocity extrapolation formulation is derived on the basis of perturbation theory by considering power law and Weibull wind speed probability distribution function. In the proposed methodology not only the mean values of the wind speeds at different elevations but also their standard deviations and the cross-correlation coefficient between different elevations are taken into consideration. The application of the presented methodology is performed for wind speed measurements at Karaburun/Istanbul, Turkey. At this location, hourly wind speed measurements are available for three different heights above the earth surface.

Accurate prediction of peak discharges due to embankment dam failure is essential to identifying and reducing potential for loss of life and damage in the downstream floodplain. Because, when a dam fails the damage is certain, but the extent of this damage cannot be evaluated in advance. The loss of life and property damage can vary depending on flood area and population. In order to cope with embankment dam breaching and to take necessary steps beforehand many researchers worked on parametric breach models based on Regression Analysis (RA) to estimate the peak outflow from a breached embankment dam since 1970s. RA is a widely-used approach that could provide acceptable results. Since, this approach bears restrictive assumptions, direct application of RA ignoring these assumptions might cause pitfalls and biased calculations. In this study, it is shown that previous works generated by RA gives biased calculations and a new alternative approach, based on Artificial Neural Networks (ANN), is suggested in replacement of classical RA, which gives more accurate results according to both numerical error criteria and scientific background.

Predicting peak breach discharge due to embankment dam failure is of vital importance for dam failure prevention and mitigation. Because, when dams fail, property damage is certain, but loss of life can vary depending on flood area and population. Many parametric breach models based on regression techniques have been developed so far. In this study, an efficient model is proposed to forecast peak discharge from the height of the water and volume of water behind the dam at failure, respectively, by using the Kriging approach. The previous studies, which consist of 13 numerical models, are used as a benchmark for testing the proposed new model, by employing five different error criteria. Moreover, a new database is compiled by extending the previous one. In addition, it is demonstrated that R², which only quantifies the dispersion between measurements and predictions, should not be considered alone for checking the model capabilities. At least, the other criteria should be employed together with R². As a result, it is shown that one can forecast the peak flow discharge with more significant accuracy by the proposed model than other previous models developed so far.