Flow estimations for the Sohu Stream using artificial neural networks
In this study, daily rainfall–runoff relationships for Sohu Stream were modelled using an artificial neural network (ANN) method by including the feed-forward back-propagation method. The ANN part was divided into two stages. During the first stage, current flows were estimated by using previously m...
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| Veröffentlicht in: | Environmental earth sciences 2012-08, Vol.66 (7), p.2031-2045 |
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| creator | Sattari, M. Taghi Apaydin, Halit Ozturk, Fazli |
| description | In this study, daily rainfall–runoff relationships for Sohu Stream were modelled using an artificial neural network (ANN) method by including the feed-forward back-propagation method. The ANN part was divided into two stages. During the first stage, current flows were estimated by using previously measured flow data. The best network architecture was found to utilise two neurons in the input layer (the delayed flows from the first and second days), two hidden layers, and one output layer (the current flow). The coefficient of determination (R 2) in this architecture was 81.4%. During the second stage, the current flows were estimated by using a combination of previously measured values for precipitation, temperature, and flows. The best architecture consisted of an input layer of 2 days of delayed precipitation, 3 days of delayed flows, and temperature of the current. The R 2 in this architecture was calculated to be 85.5%. The results of the second stage best reflected the real-world situation because they accounted for more input variables. In all models, the variables with the highest R 2 ranked as the previous flow (81.4%), previous precipitation (21.7%), and temperature. |
| doi_str_mv | 10.1007/s12665-011-1428-7 |
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The best architecture consisted of an input layer of 2 days of delayed precipitation, 3 days of delayed flows, and temperature of the current. The R 2 in this architecture was calculated to be 85.5%. The results of the second stage best reflected the real-world situation because they accounted for more input variables. 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During the second stage, the current flows were estimated by using a combination of previously measured values for precipitation, temperature, and flows. The best architecture consisted of an input layer of 2 days of delayed precipitation, 3 days of delayed flows, and temperature of the current. The R 2 in this architecture was calculated to be 85.5%. The results of the second stage best reflected the real-world situation because they accounted for more input variables. 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Taghi</au><au>Apaydin, Halit</au><au>Ozturk, Fazli</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Flow estimations for the Sohu Stream using artificial neural networks</atitle><jtitle>Environmental earth sciences</jtitle><stitle>Environ Earth Sci</stitle><date>2012-08-01</date><risdate>2012</risdate><volume>66</volume><issue>7</issue><spage>2031</spage><epage>2045</epage><pages>2031-2045</pages><issn>1866-6280</issn><eissn>1866-6299</eissn><abstract>In this study, daily rainfall–runoff relationships for Sohu Stream were modelled using an artificial neural network (ANN) method by including the feed-forward back-propagation method. The ANN part was divided into two stages. During the first stage, current flows were estimated by using previously measured flow data. The best network architecture was found to utilise two neurons in the input layer (the delayed flows from the first and second days), two hidden layers, and one output layer (the current flow). The coefficient of determination (R 2) in this architecture was 81.4%. During the second stage, the current flows were estimated by using a combination of previously measured values for precipitation, temperature, and flows. The best architecture consisted of an input layer of 2 days of delayed precipitation, 3 days of delayed flows, and temperature of the current. The R 2 in this architecture was calculated to be 85.5%. The results of the second stage best reflected the real-world situation because they accounted for more input variables. 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| subjects | Biogeosciences Earth and Environmental Science Earth Sciences Environmental science Environmental Science and Engineering Geochemistry Geology Hydrology/Water Resources Interpolation Neural networks neurons Original Article Precipitation Propagation Rain Rainfall-runoff relationships Stream flow temperature Terrestrial Pollution |
| title | Flow estimations for the Sohu Stream using artificial neural networks |
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