Estimation runoff of Bara-Ariye basin using WetSpa and artificial neural network models
Accurate estimation of watershed runoff has a crucial role in its management. Until now many researchers used different models such as integrated and distributed models, and also artificial intelligent methods to estimate basin runoff. For this purpose in this study for estimation the runoff of Bara...
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| Format: | Article |
| Language: | fas |
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University of Tabriz
2018-02-01
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| Series: | نشریه جغرافیا و برنامهریزی |
| Subjects: | |
| Online Access: | https://geoplanning.tabrizu.ac.ir/article_7108_bd3651c589387f48d3e414ff84cf46f5.pdf |
| _version_ | 1797256944870752256 |
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| author | Hosein Rahmati Samad Gholizadeh Hosein Ansari |
| author_facet | Hosein Rahmati Samad Gholizadeh Hosein Ansari |
| author_sort | Hosein Rahmati |
| collection | DOAJ |
| description | Accurate estimation of watershed runoff has a crucial role in its management. Until now many researchers used different models such as integrated and distributed models, and also artificial intelligent methods to estimate basin runoff. For this purpose in this study for estimation the runoff of Bara-Ariye basin with an area of 112 km2 and average annual rainfall of 306.72mm, two different models namely WetSpa and artificial neural network (ANN) were used. To run of the WetSpa model two categories of information, including raster maps and metrological data and for ANN model only meteorological data were used. The 5 years data were used to simulation runoff of Bara-Ariye basin. The statistical parameters such as correlation coefficient (R2), the square of the standard error of the mean (RMSE) and mean absolute error (MAE) were used for comparison results of two models. The results indicated that the WetSpa model with R2 and RMSE equal to 0.920 and 0.346 m3/s and also ANN model with R2 and RMSE equal to 0.959 and 0.310 m3/s have the ability to simulate runoff of Bara Ariye River. Also using neural network model reduced the error estimation of watershed runoff 11.6% compared with the WetSpa model. |
| first_indexed | 2024-04-24T22:29:48Z |
| format | Article |
| id | doaj.art-c3e710e410f94f8c99c98bb5b82e1314 |
| institution | Directory Open Access Journal |
| issn | 2008-8078 2717-3534 |
| language | fas |
| last_indexed | 2024-04-24T22:29:48Z |
| publishDate | 2018-02-01 |
| publisher | University of Tabriz |
| record_format | Article |
| series | نشریه جغرافیا و برنامهریزی |
| spelling | doaj.art-c3e710e410f94f8c99c98bb5b82e13142024-03-19T22:00:29ZfasUniversity of Tabrizنشریه جغرافیا و برنامهریزی2008-80782717-35342018-02-0121621171373-27108Estimation runoff of Bara-Ariye basin using WetSpa and artificial neural network modelsHosein Rahmati0Samad Gholizadeh1Hosein Ansari2Ph.D. student of Irrigation and Drainage Engineering, Shahid Chamran University of Ahvaz, Faculty of Water EngineeringAssociate Professor of Water Structures, Faculty Member of Water and Soil Department, Faculty of Agriculture, Shahroud UniversityProfessor, Department of Water Science and Engineering, Ferdowsi University of MashhadAccurate estimation of watershed runoff has a crucial role in its management. Until now many researchers used different models such as integrated and distributed models, and also artificial intelligent methods to estimate basin runoff. For this purpose in this study for estimation the runoff of Bara-Ariye basin with an area of 112 km2 and average annual rainfall of 306.72mm, two different models namely WetSpa and artificial neural network (ANN) were used. To run of the WetSpa model two categories of information, including raster maps and metrological data and for ANN model only meteorological data were used. The 5 years data were used to simulation runoff of Bara-Ariye basin. The statistical parameters such as correlation coefficient (R2), the square of the standard error of the mean (RMSE) and mean absolute error (MAE) were used for comparison results of two models. The results indicated that the WetSpa model with R2 and RMSE equal to 0.920 and 0.346 m3/s and also ANN model with R2 and RMSE equal to 0.959 and 0.310 m3/s have the ability to simulate runoff of Bara Ariye River. Also using neural network model reduced the error estimation of watershed runoff 11.6% compared with the WetSpa model.https://geoplanning.tabrizu.ac.ir/article_7108_bd3651c589387f48d3e414ff84cf46f5.pdfmodelingrunoffwetspaartificial neural network (ann)bara ariye basin |
| spellingShingle | Hosein Rahmati Samad Gholizadeh Hosein Ansari Estimation runoff of Bara-Ariye basin using WetSpa and artificial neural network models نشریه جغرافیا و برنامهریزی modeling runoff wetspa artificial neural network (ann) bara ariye basin |
| title | Estimation runoff of Bara-Ariye basin using WetSpa and artificial neural network models |
| title_full | Estimation runoff of Bara-Ariye basin using WetSpa and artificial neural network models |
| title_fullStr | Estimation runoff of Bara-Ariye basin using WetSpa and artificial neural network models |
| title_full_unstemmed | Estimation runoff of Bara-Ariye basin using WetSpa and artificial neural network models |
| title_short | Estimation runoff of Bara-Ariye basin using WetSpa and artificial neural network models |
| title_sort | estimation runoff of bara ariye basin using wetspa and artificial neural network models |
| topic | modeling runoff wetspa artificial neural network (ann) bara ariye basin |
| url | https://geoplanning.tabrizu.ac.ir/article_7108_bd3651c589387f48d3e414ff84cf46f5.pdf |
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