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| Titel |
Comparison of Artificial Intelligence Techniques for river flow forecasting |
| VerfasserIn |
M. Firat |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 12, no. 1 ; Nr. 12, no. 1 (2008-01-30), S.123-139 |
| Datensatznummer |
250010464
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| Publikation (Nr.) |
 copernicus.org/hess-12-123-2008.pdf |
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| Zusammenfassung |
| The use of Artificial Intelligence methods is becoming increasingly common
in the modeling and forecasting of hydrological and water resource
processes. In this study, applicability of Adaptive Neuro Fuzzy Inference
System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized
Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN),
and Auto-Regressive (AR) models for forecasting of daily river flow
is investigated and Seyhan River and Cine River was chosen as case study
area. For the Seyhan River, the forecasting models are established using
combinations of antecedent daily river flow records. On the other hand, for
the Cine River, daily river flow and rainfall records are used in input
layer. For both stations, the data sets are divided into three subsets,
training, testing and verification data set. The river flow forecasting
models having various input structures are trained and tested to investigate
the applicability of ANFIS and ANN and AR methods. The results of all models
for both training and testing are evaluated and the best fit input
structures and methods for both stations are determined according to
criteria of performance evaluation. Moreover the best fit forecasting models
are also verified by verification set which was not used in training and
testing processes and compared according to criteria. The results
demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting
models, and ANFIS can be successfully applied and provide high accuracy and
reliability for daily river flow forecasting. |
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