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A comparative study of artificial neural network techniques for river stage forecasting
Although artificial neural networks have been applied to problems within hydrology for over ten years, there is little consensus on the 'best' type of neural network model to use and the most effective means of training the chosen model. In order to explore the different approaches neural...
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| container_end_page | 2670 vol. 4 |
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| creator | Dawson, C.W. See, L.M. Abrahart, R.J. Wilby, R.L. Shamseldin, A.Y. Anctil, F. Belbachir, A.N. Bowden, G. Dandy, G. Lauzon, N. Maier, H. |
| description | Although artificial neural networks have been applied to problems within hydrology for over ten years, there is little consensus on the 'best' type of neural network model to use and the most effective means of training the chosen model. In order to explore the different approaches neural network modellers use to forecasting river stage, an international comparison study was undertaken during 2004. This research was based on a set of rainfall and river stage data covering three winter periods for an unidentified river basin in England (with a catchment of 331,500 Ha in the north of the country), sampled at 15 minute intervals. Several neural network enthusiasts took part in the study from a number of different countries. The preferred methodologies and forecasting outputs from a number of 'blind' models of river stage developed by the participants have been collated and are presented in this paper. |
| doi_str_mv | 10.1109/IJCNN.2005.1556324 |
| format | conference_proceeding |
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The preferred methodologies and forecasting outputs from a number of 'blind' models of river stage developed by the participants have been collated and are presented in this paper.</description><subject>Artificial neural networks</subject><subject>Calibration</subject><subject>Civil engineering</subject><subject>Computer science</subject><subject>Geography</subject><subject>Hydrology</subject><subject>Predictive models</subject><subject>Rivers</subject><subject>Technology forecasting</subject><subject>Testing</subject><issn>2161-4393</issn><issn>2161-4407</issn><isbn>0780390482</isbn><isbn>9780780390485</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1kMtOwzAURC0eEm3hB2DjH0i4fsfLKuJRVJVNJZaV7djF0CbFcUD9ewqU1dFodGYxCF0TKAkBfTt7qheLkgKIkgghGeUnaESJJAXnoE7RGFQFTAOv6Nl_wTS7QOO-fwOgTGs2Qi9T7LrtziST46fHfR6aPe4CNinHEF00G9z6If0if3XpHWfvXtv4Mfgehy7hdNDSwTNr_5O9M32O7foSnQez6f3VkRO0vL9b1o_F_PlhVk_nRaSc5IKy0BApZBWoJayRqmqCC0GAVZw6K5QUOgBoYwN3FKxoKmW9tUI44hVlE3TzNxu996tdiluT9qvjH-wbDmlUZA</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Dawson, C.W.</creator><creator>See, L.M.</creator><creator>Abrahart, R.J.</creator><creator>Wilby, R.L.</creator><creator>Shamseldin, A.Y.</creator><creator>Anctil, F.</creator><creator>Belbachir, A.N.</creator><creator>Bowden, G.</creator><creator>Dandy, G.</creator><creator>Lauzon, N.</creator><creator>Maier, H.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2005</creationdate><title>A comparative study of artificial neural network techniques for river stage forecasting</title><author>Dawson, C.W. ; See, L.M. ; Abrahart, R.J. ; Wilby, R.L. ; Shamseldin, A.Y. ; Anctil, F. ; Belbachir, A.N. ; Bowden, G. ; Dandy, G. ; Lauzon, N. ; Maier, H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i241t-23fd16568f2b13d678dfcff50b742cb57659f009abf4c20b5d87bebb55c1e723</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Artificial neural networks</topic><topic>Calibration</topic><topic>Civil engineering</topic><topic>Computer science</topic><topic>Geography</topic><topic>Hydrology</topic><topic>Predictive models</topic><topic>Rivers</topic><topic>Technology forecasting</topic><topic>Testing</topic><toplevel>online_resources</toplevel><creatorcontrib>Dawson, C.W.</creatorcontrib><creatorcontrib>See, L.M.</creatorcontrib><creatorcontrib>Abrahart, R.J.</creatorcontrib><creatorcontrib>Wilby, R.L.</creatorcontrib><creatorcontrib>Shamseldin, A.Y.</creatorcontrib><creatorcontrib>Anctil, F.</creatorcontrib><creatorcontrib>Belbachir, A.N.</creatorcontrib><creatorcontrib>Bowden, G.</creatorcontrib><creatorcontrib>Dandy, G.</creatorcontrib><creatorcontrib>Lauzon, N.</creatorcontrib><creatorcontrib>Maier, H.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dawson, C.W.</au><au>See, L.M.</au><au>Abrahart, R.J.</au><au>Wilby, R.L.</au><au>Shamseldin, A.Y.</au><au>Anctil, F.</au><au>Belbachir, A.N.</au><au>Bowden, G.</au><au>Dandy, G.</au><au>Lauzon, N.</au><au>Maier, H.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A comparative study of artificial neural network techniques for river stage forecasting</atitle><btitle>Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005</btitle><stitle>IJCNN</stitle><date>2005</date><risdate>2005</risdate><volume>4</volume><spage>2666</spage><epage>2670 vol. 4</epage><pages>2666-2670 vol. 4</pages><issn>2161-4393</issn><eissn>2161-4407</eissn><isbn>0780390482</isbn><isbn>9780780390485</isbn><abstract>Although artificial neural networks have been applied to problems within hydrology for over ten years, there is little consensus on the 'best' type of neural network model to use and the most effective means of training the chosen model. In order to explore the different approaches neural network modellers use to forecasting river stage, an international comparison study was undertaken during 2004. This research was based on a set of rainfall and river stage data covering three winter periods for an unidentified river basin in England (with a catchment of 331,500 Ha in the north of the country), sampled at 15 minute intervals. Several neural network enthusiasts took part in the study from a number of different countries. The preferred methodologies and forecasting outputs from a number of 'blind' models of river stage developed by the participants have been collated and are presented in this paper.</abstract><pub>IEEE</pub><doi>10.1109/IJCNN.2005.1556324</doi></addata></record> |
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| identifier | ISSN: 2161-4393 |
| ispartof | Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005, 2005, Vol.4, p.2666-2670 vol. 4 |
| issn | 2161-4393 2161-4407 |
| language | eng |
| recordid | cdi_ieee_primary_1556324 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Calibration Civil engineering Computer science Geography Hydrology Predictive models Rivers Technology forecasting Testing |
| title | A comparative study of artificial neural network techniques for river stage forecasting |
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