Yıl 2020, Cilt 11 , Sayı , Sayfalar 125 - 130 2020-12-31

Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection

Levent LATIFOGLU [1]


Water resources are needed to maintain the human life and the management the ecologic system for many areas. The most economical use, protection and development of water resources have a great importance for hydrological studies. Variable such as stream flow data are commonly used in hydrology. Accurate stream flow estimation is very important in terms of planning and management of water resources and minimizing the effects of natural disasters such as drought and flood. Monthly river flow data obtained from the Sakarya basin on Porsuk River between 1970-2000 years were used for the estimation study. For this purpose, forecasting performance has been analyzed using Adaptive Network Based Fuzzy Logic Inference System (ANFIS) and Artificial Neural Networks (ANN) models and performances of these two models were compared. In addition, the average monthly stream flow data, standard deviation values of these data were also used in the forecasting study and applied as an input to ANFIS and ANN models. For a one ahead estimation, models have been developed with different input combinations of 1-3 past value of stream flow data and standard deviation values. In this study, mean square error (mse), mean absolute error (mae) and correlation coefficient parameters were used to evaluate the performance of the models. According to the obtained results, it is seen that the ANN model has better forecasting performance for two inputs according to mse and mae parameters and for three inputs according to R and R2 parameters. Also, it is seen that the ANFIS model has the best performance for two inputs according to mse, mae, R and R2 parameters. There has been some improvement in the forecast performance if the monthly average river stream flow data as well as the standard deviation data has been applied as an input to the model
Stream flow forecasting, Adaptive Network Based Fuzzy Logic Inference System Method (ANFIS), Artificial Neural Networks (ANN)
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Birincil Dil en
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Yazar: Levent LATIFOGLU
Kurum: ERCIYES UNIVERSITY
Ülke: Turkey


Tarihler

Yayımlanma Tarihi : 31 Aralık 2020

Bibtex @konferans bildirisi { epstem837506, journal = {The Eurasia Proceedings of Science Technology Engineering and Mathematics}, issn = {}, eissn = {2602-3199}, address = {isresoffice@gmail.com}, publisher = {ISRES Organizasyon Turizm Eğitim Danışmanlık Ltd. Şti.}, year = {2020}, volume = {11}, pages = {125 - 130}, doi = {}, title = {Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection}, key = {cite}, author = {Latıfoglu, Levent} }
APA Latıfoglu, L . (2020). Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection . The Eurasia Proceedings of Science Technology Engineering and Mathematics , 11 , 125-130 . Retrieved from https://dergipark.org.tr/tr/pub/epstem/issue/58065/837506
MLA Latıfoglu, L . "Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection" . The Eurasia Proceedings of Science Technology Engineering and Mathematics 11 (2020 ): 125-130 <https://dergipark.org.tr/tr/pub/epstem/issue/58065/837506>
Chicago Latıfoglu, L . "Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection". The Eurasia Proceedings of Science Technology Engineering and Mathematics 11 (2020 ): 125-130
RIS TY - JOUR T1 - Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection AU - Levent Latıfoglu Y1 - 2020 PY - 2020 N1 - DO - T2 - The Eurasia Proceedings of Science Technology Engineering and Mathematics JF - Journal JO - JOR SP - 125 EP - 130 VL - 11 IS - SN - -2602-3199 M3 - UR - Y2 - 2020 ER -
EndNote %0 The Eurasia Proceedings of Science Technology Engineering and Mathematics Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection %A Levent Latıfoglu %T Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection %D 2020 %J The Eurasia Proceedings of Science Technology Engineering and Mathematics %P -2602-3199 %V 11 %N %R %U
ISNAD Latıfoglu, Levent . "Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection". The Eurasia Proceedings of Science Technology Engineering and Mathematics 11 / (Aralık 2020): 125-130 .
AMA Latıfoglu L . Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection. EPSTEM. 2020; 11: 125-130.
Vancouver Latıfoglu L . Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection. The Eurasia Proceedings of Science Technology Engineering and Mathematics. 2020; 11: 125-130.
IEEE L. Latıfoglu , "Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection", The Eurasia Proceedings of Science Technology Engineering and Mathematics, c. 11, ss. 125-130, Ara. 2021