In Turkey, many enterprisers started to make investment on renewable energy systems after new legal regulations and stimulus packages about production of renewable energy were introduced. Out of many alternatives, production of electricity via wind farms is one of the leading systems. For these systems, the wind speed values measured prior to the establishment of the farms are extremely important in both decision making and in the projection of the investment. However, the measurement of the wind speed at different heights is a time consuming and expensive process. For this reason, the success of the techniques predicting the wind speeds is fairly important in fast and reliable decision-making for investment in wind farms. In this study, the annual wind speed values of Kutahya, one of the regions in Turkey that has potential for wind energy at two different heights, were used and with the help of speed values at 10 m, wind speed values at 30 m of height were predicted by seven different machine learning methods. The results of the analysis were compared with each other. The results show that support vector machines is a successful technique in the prediction of the wind speed for different heights.
Particle swarm optimization (PSO) algorithm is a heuristic optimization technique based on colony intelligence, developed through inspiration from social behaviors of bird flocks and fish schools. It is widely used in problems in which the optimal value of an objective function is searched. Geometrically nonlinear analysis of trusses is a problem of this kind. The deflected shape of the truss where potential energy value is minimal is known to correspond to the stable equilibrium position of the system analyzed. The objective of this study is to explore the success of PSO using this minimum total potential energy principle, in finding good solutions to geometrically nonlinear truss problems. For this purpose analyses are conducted on three structures, two plane trusses and a space truss. The results obtained show that in case of using 20 or more particles, PSO produces very good and robust solutions.
Trafik kazaları dünya çapında bir endişe kaynağı olup, genç ve yetişkinler arasında önde gelen ölüm ve yaralanma nedenidir. Dünya Sağlık Örgütü'nün (WHO) 2018 yılında yol güvenliğine ilişkin küresel durum raporuna göre, trafik kazaları nedeniyle her yıl yaklaşık 1,35 milyon kişi hayatını kaybetmekte ve 50 milyon kişi yaralanmaktadır. Karayolu trafik sistemi, insan, araç, yol ve doğal çevre gibi kapsamlı faktörleri içeren karmaşık bir sistemdir. Bu karmaşık sistem uygun iyileştirmeler olmadığı taktirde can kayıplarına, yaralanmalara, maddi hasara ve trafik sıkışıklığına neden olacaktır. Bu nedenle, trafik güvenliğini artırmak için trafik kazalarını etkileyen etkili faktörlerin analiz edilmesi gerekmektedir. Mevcut literatürde trafik kazalarını etkileyen ekonomi, iklim, yol yapısı, trafik bilgileri ve trafik güvenliği kanunları gibi çok sayıda faktör bulunmaktadır. Bu çalışmada trafik kazalarına etki eden sürücü dışındaki kriterler ve bunların alt kriterleri belirlendi. Ardından çok kriterli karar verme yöntemleri olan BWM ve SWARA metotları ile trafik kazalarına etki eden faktörlerin ağrılıkları hesaplanarak karayolu kazalarının azaltılması için öneri sunuldu.
The rapid growth in the number of drivers and vehicles in the population and the need for easy transportation of people increases the importance of public transportation. Traffic becomes a growing problem in Istanbul which is Turkey's greatest urban settlement area. Decisions on investments and projections for the public transportation should be well planned by considering the total number of passengers and the variations in the demand on the different regions. The success of this planning is directly related to the accurate passenger demand forecasting. In this study, machine learning algorithms are tested in a real world demand forecasting problem where hourly passenger demands collected from two transfer stations of a public transportation system. The machine learning techniques are run in the WEKA software and the performance of methods are compared by MAE and RMSE statistical measures. The results show that the bagging based decision tree methods and rules methods have the best performance.
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