- Eklemek veya çıkarmak istediğiniz kriterleriniz için 'Dahil' / 'Hariç' seçeneğini kullanabilirsiniz. Sorgu satırları birbirine 'VE' bağlacı ile bağlıdır. - İptal tuşuna basarak normal aramaya dönebilirsiniz.
Soft computing models are known as an efficient tool for modelling temporal and spatial variation of surface water quality variables and particularly in rivers. These model’s performance relies on how effective their simulation processes are accomplished. Fuzzy logic approach is one of the authoritative intelligent model in solving complex problems that deal with uncertainty and vagueness data. River water quality nature is involved with high stochasticity and redundancy due to the its correlation with several hydrological and environmental aspects. Yet, the fuzzy logic theory can give robust ...Daha fazlası
The ability of three different heuristic regression methods, least-square support vector regression (LSSVR), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree), was investigated in forecasting hydrological time series. In the study, daily streamflow data from two stations in Turkey were used by employing cross validation method. Models were compared with each other with respect to root-mean-square error, mean absolute error and determination coefficient. The LSSVR was found to be superior to the MARS and M5Tree models in daily streamflow forecasting. The effect of perio ...Daha fazlası
This study investigates the applicability of three different soft computing methods, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS), and M5 Model Tree (M5-Tree), in forecasting SO2 concentration. These models were applied to monthly data obtained from Janakpuri, Nizamuddin, and Shahzadabad, located in Delhi, India. The models were compared with each other using the cross validation method with respect to root mean square error, mean absolute error, and correlation coefficient. According to the comparison, LSSVR provided better accuracy than the ...Daha fazlası
This paper investigates the accuracy of three different adaptive neuro-fuzzy inference systems (ANFISs), ANFIS with grid partition (ANFIS-GP), ANFIS with substructive clustering (ANFIS-SC), and ANFIS with fuzzy c means (ANFIS-FCM) in estimation of long-term monthly air temperatures. Data of 71 stations in Turkey are used in the applications. The periodicity (month of the year) and geographical variables (latitude, longitude, and altitude) are used as inputs to the models. ANFIS models are also compared with artificial neural networks (ANNs) and multilinear regression (MLR). Three ANFIS methods ...Daha fazlası
6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve cerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.