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Doç. Dr. Vahdettin DemirKTO KARATAY ÜNİVERSİTESİ/MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ/İNŞAAT MÜHENDİSLİĞİ BÖLÜMÜ/İNŞAAT MÜHENDİSLİĞİ PR. (TAM BURSLU)/
In this study, monthly solar radiation (SR) estimation was performed using five different machine learning-based approaches. The models used are support vector machine regression (SVMR), long short-term memory (LSTM), Gaussian process regression (GPR), extreme learning machines (ELM) and K-nearest neighbors (KNN). Modeling of these approaches was carried out in two stages. In the first stage, VIF analysis was carried out to develop the model. Thus, the input parameters that decrease the performance of the model are removed. In the second stage, remaining input parameters such as meteorological ...Daha fazlası
This study aims to investigate the trend of water-level changes in lakes (Lake Tuz and Lake Beyşehir) and sinkholes (Timraş and Kızören) in the Konya Closed Basin located in Turkey. Water-level changes in these lakes and sinkholes were investigated along with changes in meteorological parameters (precipitation, temperature, and evaporation) and groundwater trends that indicate the climate in the region. Several statistical tests can be used to determine the signifcance of hydrological trends over time. These tests are divided into two categories: parametric and nonparametric. In this study, th ...Daha fazlası
This study aims to carry out regional intensity−duration−frequency (IDF) equality using the relationship with IDF obtained from point frequency analysis. Eleven empirical equations used in the literature for seven climate regions of Turkey were calibrated using particle swarm optimization (PSO) and genetic algorithm (GA) optimization techniques and the obtained results were compared. In addition, in this study, new regional IDF equations were obtained for each region utilizing MultiGene Genetic Programming (MGGP) method. Finally, Kruskal–Wallis (KW) test was applied to the IDF values obtained ...Daha fazlası
Hydrological processes forecasting is an essential step for better water management and sustainability. Among several hydrological processes, lake water level (LWL) forecasting is one of the significant processes within a particular catchment. The complexity of the LWL fluctuation is owing to the diversity of the influential parameters including climate, hydrology and some other morphology. In this study, several versions of neurocomputing intelligence models are developed for LWL fluctuation forecasting at five great lakes Lake Superior, Lake Michigan, Lake Huron, Lake Erie, and Lake Ontario, ...Daha fazlası
This study investigates the accuracy of three diferent techniques with the periodicity component for estimating monthly lake levels. The three techniques are multivariate adaptive regression splines (MARS), least-square support vector regression (LSSVR), and M5 model tree (M5-tree). Data from Lake Michigan, located in the USA, is used in the analysis. In the frst stage of modeling, three techniques were applied to forecast monthly lake level fuctuations up to 8 months ahead of time intervals. In the second stage, the infuence of the periodicity component was applied (month number of the year, ...Daha fazlası
6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve cerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.