Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/7282
Title: | Comparison of Artificial Intelligence Techniques for river flow forecasting | Authors: | Fırat, Mahmut | Keywords: | artificial intelligence artificial neural network comparative study forecasting method hydrological modeling performance assessment rainfall river flow testing method Aydin Cine River Eurasia Seyhan River Turkey |
Publisher: | Copernicus GmbH | Abstract: | 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. | URI: | https://hdl.handle.net/11499/7282 https://doi.org/10.5194/hess-12-123-2008 |
ISSN: | 1027-5606 |
Appears in Collections: | Mühendislik Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
Files in This Item:
File | Size | Format | |
---|---|---|---|
hess-12-123-2008.pdf | 2.36 MB | Adobe PDF | View/Open |
CORE Recommender
SCOPUSTM
Citations
72
checked on Oct 13, 2024
WEB OF SCIENCETM
Citations
61
checked on Oct 20, 2024
Page view(s)
36
checked on Aug 24, 2024
Download(s)
16
checked on Aug 24, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.