genetic programming approach to rainfall-runoff modelling
Planning for sustainable development of water resources relies crucially on the data available. Continuous hydrologic simulation based on conceptual models has proved to be the appropriate tool for studying rainfall-runoff processes and for providing necessary data. In recent years, artificial neura...
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| Veröffentlicht in: | Water resources management 1999-06, Vol.13 (3), p.219-231 |
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| creator | Savic, D.A Walters, G.A Davidson, J.W |
| description | Planning for sustainable development of water resources relies crucially on the data available. Continuous hydrologic simulation based on conceptual models has proved to be the appropriate tool for studying rainfall-runoff processes and for providing necessary data. In recent years, artificial neural networks have emerged as a novel identification technique for the modelling of hydrological processes. However, they represent their knowledge in terms of a weight matrix that is not accessible to human understanding at present. This paper introduces genetic programming, which is an evolutionary computing method that provides a 'transparent' and structured system identification, to rainfall-runoff modelling. The genetic-programming approach is applied to flow prediction for the Kirkton catchment in Scotland (U.K.). The results obtained are compared to those attained using two optimally calibrated conceptual models and an artificial neural network. Correlations identified using data-driven approaches (genetic programming and neural network) are surprising in their consistency considering the relative size of the models and the number of variables included. These results also compare favourably with the conceptual models. |
| doi_str_mv | 10.1023/A:1008132509589 |
| format | Article |
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Continuous hydrologic simulation based on conceptual models has proved to be the appropriate tool for studying rainfall-runoff processes and for providing necessary data. In recent years, artificial neural networks have emerged as a novel identification technique for the modelling of hydrological processes. However, they represent their knowledge in terms of a weight matrix that is not accessible to human understanding at present. This paper introduces genetic programming, which is an evolutionary computing method that provides a 'transparent' and structured system identification, to rainfall-runoff modelling. The genetic-programming approach is applied to flow prediction for the Kirkton catchment in Scotland (U.K.). The results obtained are compared to those attained using two optimally calibrated conceptual models and an artificial neural network. 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Correlations identified using data-driven approaches (genetic programming and neural network) are surprising in their consistency considering the relative size of the models and the number of variables included. These results also compare favourably with the conceptual models.</description><subject>British Isles, Scotland, Kirkton R</subject><subject>computer simulation</subject><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Exact sciences and technology</subject><subject>Hydrology</subject><subject>Hydrology. Hydrogeology</subject><subject>Neural networks</subject><subject>programming</subject><subject>rain</subject><subject>Rainfall-runoff relationships</subject><subject>Runoff</subject><subject>simulation models</subject><subject>stream flow</subject><subject>Studies</subject><subject>Sustainable development</subject><subject>Water resources development</subject><subject>watershed hydrology</subject><subject>watersheds</subject><issn>0920-4741</issn><issn>1573-1650</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNpdzktLAzEQwPEgCtbq2aOLiLfVmTw2GW-l-IKCB-15SbPZdcs-arI9-O0NtCdPwww_hj9j1wgPCFw8Lp4QwKDgCkgZOmEzVFrkWCg4ZTMgDrnUEs_ZRYxbgIQJZowaP_ipddkujE2wfd8OTWZ3abPuO5vGLNh2qG3X5WE_jHWd9WPluy6pS3aW7tFfHeecrV-ev5Zv-erj9X25WOVWaJpyoRRVtQIlLK98zYWVXEpPoFFLvuGEznFunLayosI4vqGNQyO8A1NQxcWc3R_-pqafvY9T2bfRpQY7-HEfS9TCCERK8PYf3I77MKS2UisOhdBGJXR3RDY629XBDq6N5S60vQ2_JRpShdSJ3RxYbcfSNiGR9ScHFMBJIBQk_gCFz2sE</recordid><startdate>19990601</startdate><enddate>19990601</enddate><creator>Savic, D.A</creator><creator>Walters, G.A</creator><creator>Davidson, J.W</creator><general>Springer</general><general>Springer Nature B.V</general><scope>FBQ</scope><scope>IQODW</scope><scope>3V.</scope><scope>7QH</scope><scope>7ST</scope><scope>7UA</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>H97</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>KR7</scope><scope>L.-</scope><scope>L.G</scope><scope>L6V</scope><scope>LK8</scope><scope>M0C</scope><scope>M2P</scope><scope>M7P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope><scope>H96</scope></search><sort><creationdate>19990601</creationdate><title>genetic programming approach to rainfall-runoff modelling</title><author>Savic, D.A ; Walters, G.A ; Davidson, J.W</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a379t-3559df5053a2def23a4244e9071742b291cc228c7a4d968c2b9bc183ec0869d23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>British Isles, Scotland, Kirkton R</topic><topic>computer simulation</topic><topic>Earth sciences</topic><topic>Earth, ocean, space</topic><topic>Exact sciences and technology</topic><topic>Hydrology</topic><topic>Hydrology. Hydrogeology</topic><topic>Neural networks</topic><topic>programming</topic><topic>rain</topic><topic>Rainfall-runoff relationships</topic><topic>Runoff</topic><topic>simulation models</topic><topic>stream flow</topic><topic>Studies</topic><topic>Sustainable development</topic><topic>Water resources development</topic><topic>watershed hydrology</topic><topic>watersheds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Savic, D.A</creatorcontrib><creatorcontrib>Walters, G.A</creatorcontrib><creatorcontrib>Davidson, J.W</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection (ProQuest)</collection><collection>Natural Science Collection (ProQuest)</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ABI/INFORM Professional Advanced</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>ABI/INFORM Global</collection><collection>Science Database (ProQuest)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><jtitle>Water resources management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Savic, D.A</au><au>Walters, G.A</au><au>Davidson, J.W</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>genetic programming approach to rainfall-runoff modelling</atitle><jtitle>Water resources management</jtitle><date>1999-06-01</date><risdate>1999</risdate><volume>13</volume><issue>3</issue><spage>219</spage><epage>231</epage><pages>219-231</pages><issn>0920-4741</issn><eissn>1573-1650</eissn><coden>WRMAEJ</coden><abstract>Planning for sustainable development of water resources relies crucially on the data available. Continuous hydrologic simulation based on conceptual models has proved to be the appropriate tool for studying rainfall-runoff processes and for providing necessary data. In recent years, artificial neural networks have emerged as a novel identification technique for the modelling of hydrological processes. However, they represent their knowledge in terms of a weight matrix that is not accessible to human understanding at present. This paper introduces genetic programming, which is an evolutionary computing method that provides a 'transparent' and structured system identification, to rainfall-runoff modelling. The genetic-programming approach is applied to flow prediction for the Kirkton catchment in Scotland (U.K.). The results obtained are compared to those attained using two optimally calibrated conceptual models and an artificial neural network. 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| ispartof | Water resources management, 1999-06, Vol.13 (3), p.219-231 |
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| subjects | British Isles, Scotland, Kirkton R computer simulation Earth sciences Earth, ocean, space Exact sciences and technology Hydrology Hydrology. Hydrogeology Neural networks programming rain Rainfall-runoff relationships Runoff simulation models stream flow Studies Sustainable development Water resources development watershed hydrology watersheds |
| title | genetic programming approach to rainfall-runoff modelling |
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