http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Özgür Kisi (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
A Combined Generalized Regression Neural Network Wavelet Model for Monthly Streamflow Prediction
Özgür Kisi 대한토목학회 2011 KSCE Journal of Civil Engineering Vol.15 No.8
The ability of a combined model, Wavelet-Generalized Regression Neural Network (WGRNN), is investigated in the current study for the prediction of monthly streamflows. The WGRNN model is obtained by combining two methods, Discrete Wavelet Transform (DWT) and Generalized Regression Neural Network (GRNN), for one-month-ahead streamflow forecasting. The monthly flow data of two stations, the Gerdelli Station on the Canakdere River and the Isakoy Station on the Goksudere River, in the Eastern Black Sea region of Turkey are used in the study. The forecasts of the WGRNN model are tested using the Root Mean Square Error (RMSE),Variance Account For (VAF) and correlation coefficient (R) statistics and the results are compared with those of the single GRNN and Feed Forward Neural Network (FFNN). The comparison results revealed that the WGRNN performs better than the GRNN and FFNN models in monthly streamflow prediction. For the Gerdelli and Isakoy stations, it is found that the WGRNN models with RMSE = 5.31 m^3/s, VAF = 52.3%, R = 0.728 and RMSE = 3.36 m^3/s, VAF = 55.1%, R = 0.742 in the test period are superior in forecasting monthly streamflows than the best accurate GRNN models with RMSE = 6.39 m3/s, VAF = 30.1%, R = 0.553 and RMSE = 4.19 m^3/s, VAF = 30.1%, R = 0.549, respectively.
-
Estimation of Dissolved Oxygen by using Neural Networks and Neuro Fuzzy Computing Techniques
Murat AY,Özgür Kisi 대한토목학회 2017 KSCE JOURNAL OF CIVIL ENGINEERING Vol.21 No.5
Dissolved oxygen, one of the most important water quality parameters, is a crucial parameter for the aquatic ecosystems. In this study, some advanced chemometric techniques included in a multi-layer perceptron, radial basis neural network, and two different adaptive neuro-fuzzy inference system methods are developed to model dissolved oxygen concentration. Moreover the estimations of these models are compared with the multiple linear regression. In this context, monthly mean quantities of the temperature, pH, electrical conductivity, discharge and dissolved oxygen data recorded at Broad River near Carlisle, SC in USA are used. The accuracy of the models is compared with one other by using determination coefficient, mean absolute error, root mean square error and mean absolute relative error statistics. Results indicate that radial basis neural network method performs better than the other methods in modelling monthly mean dissolved oxygen concentration. The temperature, pH, electrical conductivity, and discharge are found to be effective on dissolved oxygen concentration.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
