Application of Bayesian Networks and Genetic Programming for Predicting Daily River Flow (Case Study: Barandoozchay River)
Accurate estimation of river discharge is an important issue in forecasting of drought and floods, designing of water structures, dam reservoir operation and sediment control. So far, several methods such as time series models, Artificial Neural Networks, Fuzzy models and Genetic programming have be...
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| Format: | Article |
| Language: | fas |
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Shahid Chamran University of Ahvaz
2016-12-01
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| Series: | علوم و مهندسی آبیاری |
| Subjects: | |
| Online Access: | http://jise.scu.ac.ir/article_12509_a0d616449ae4a648961605e8b523bfb7.pdf |
| _version_ | 1818775050459807744 |
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| author | Farshad Ahmadi Feraidoon Radmanesh Rasoul mir abbasi najf abadi |
| author_facet | Farshad Ahmadi Feraidoon Radmanesh Rasoul mir abbasi najf abadi |
| author_sort | Farshad Ahmadi |
| collection | DOAJ |
| description | Accurate estimation of river discharge is an important issue in forecasting of drought and floods, designing of water structures, dam reservoir operation and sediment control. So far, several methods such as time series models, Artificial Neural Networks, Fuzzy models and Genetic programming have been used for accurate predicting of river flow. In this study, Genetic Programming and Bayesian Networks methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. For assessing the role of memory in increasing or reducing of model accuracy, we tested different combinations of input variables. The results showed that at first, the accuracy of models increase with increasing of memory, as the most accuracy obtained in third combination of input variables in both of methods. After that with increasing of memory the accuracy of models decreased. Comparing the performance of GP and BNs models indicated that the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than BNs method with R=0.964 and RMSE=1.96 (m3/s). In addition, the performance of GP method was better than BNs method in predicting minimum and average discharges. |
| first_indexed | 2024-12-18T10:50:52Z |
| format | Article |
| id | doaj.art-71c7ce6efaee417ba27612d276645241 |
| institution | Directory Open Access Journal |
| issn | 2588-5952 2588-5960 |
| language | fas |
| last_indexed | 2024-12-18T10:50:52Z |
| publishDate | 2016-12-01 |
| publisher | Shahid Chamran University of Ahvaz |
| record_format | Article |
| series | علوم و مهندسی آبیاری |
| spelling | doaj.art-71c7ce6efaee417ba27612d2766452412022-12-21T21:10:27ZfasShahid Chamran University of Ahvazعلوم و مهندسی آبیاری2588-59522588-59602016-12-0139421322310.22055/jise.2016.1250912509Application of Bayesian Networks and Genetic Programming for Predicting Daily River Flow (Case Study: Barandoozchay River)Farshad Ahmadi0Feraidoon Radmanesh1Rasoul mir abbasi najf abadi2دانشجوی دکتری مهندسی منابع آب، دانشگاه شهید چمران اهواز .دانشیار گروه مهندسی منابع آب، دانشگاه شهید چمران اهواز.استادیار گروه مهندسی آب، دانشگاه شهر کرد.Accurate estimation of river discharge is an important issue in forecasting of drought and floods, designing of water structures, dam reservoir operation and sediment control. So far, several methods such as time series models, Artificial Neural Networks, Fuzzy models and Genetic programming have been used for accurate predicting of river flow. In this study, Genetic Programming and Bayesian Networks methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. For assessing the role of memory in increasing or reducing of model accuracy, we tested different combinations of input variables. The results showed that at first, the accuracy of models increase with increasing of memory, as the most accuracy obtained in third combination of input variables in both of methods. After that with increasing of memory the accuracy of models decreased. Comparing the performance of GP and BNs models indicated that the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than BNs method with R=0.964 and RMSE=1.96 (m3/s). In addition, the performance of GP method was better than BNs method in predicting minimum and average discharges.http://jise.scu.ac.ir/article_12509_a0d616449ae4a648961605e8b523bfb7.pdfgenetic programmingdaily discharge forecastingbayesian networksbarandoozchay river |
| spellingShingle | Farshad Ahmadi Feraidoon Radmanesh Rasoul mir abbasi najf abadi Application of Bayesian Networks and Genetic Programming for Predicting Daily River Flow (Case Study: Barandoozchay River) علوم و مهندسی آبیاری genetic programming daily discharge forecasting bayesian networks barandoozchay river |
| title | Application of Bayesian Networks and Genetic Programming for Predicting Daily River Flow (Case Study: Barandoozchay River) |
| title_full | Application of Bayesian Networks and Genetic Programming for Predicting Daily River Flow (Case Study: Barandoozchay River) |
| title_fullStr | Application of Bayesian Networks and Genetic Programming for Predicting Daily River Flow (Case Study: Barandoozchay River) |
| title_full_unstemmed | Application of Bayesian Networks and Genetic Programming for Predicting Daily River Flow (Case Study: Barandoozchay River) |
| title_short | Application of Bayesian Networks and Genetic Programming for Predicting Daily River Flow (Case Study: Barandoozchay River) |
| title_sort | application of bayesian networks and genetic programming for predicting daily river flow case study barandoozchay river |
| topic | genetic programming daily discharge forecasting bayesian networks barandoozchay river |
| url | http://jise.scu.ac.ir/article_12509_a0d616449ae4a648961605e8b523bfb7.pdf |
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