PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS
: The cascade correlation neural network was used to predict the two-year peak discharge (Q2) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 1...
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| Autor*in: |
Muttiah, Ranjan S. [verfasserIn] Srinivasan, Raghavan [verfasserIn] Allen, Peter M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Erschienen: |
Oxford, UK: Blackwell Publishing Ltd ; 1997 |
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| Schlagwörter: |
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| Umfang: |
Online-Ressource |
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| Reproduktion: |
2007 ; Blackwell Publishing Journal Backfiles 1879-2005 |
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| Übergeordnetes Werk: |
In: Journal of the American Water Resources Association - American Water Resources Association ; GKD-ID: 11654, Middleburg VA : Assoc., 1967, 33(1997), 3, Seite 0 |
| Übergeordnetes Werk: |
volume:33 ; year:1997 ; number:3 ; pages:0 |
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| DOI / URN: |
10.1111/j.1752-1688.1997.tb03537.x |
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NLEJ243381662 |
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| 520 | |a : The cascade correlation neural network was used to predict the two-year peak discharge (Q2) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q2 by regression. Our research shows Q2 was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds. | ||
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10.1111/j.1752-1688.1997.tb03537.x doi (DE-627)NLEJ243381662 DE-627 ger DE-627 rakwb Muttiah, Ranjan S. verfasserin aut PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS Oxford, UK Blackwell Publishing Ltd 1997 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier : The cascade correlation neural network was used to predict the two-year peak discharge (Q2) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q2 by regression. Our research shows Q2 was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds. 2007 Blackwell Publishing Journal Backfiles 1879-2005 |2007|||||||||| neural networks Srinivasan, Raghavan verfasserin aut Allen, Peter M. verfasserin aut In American Water Resources Association ; GKD-ID: 11654 Journal of the American Water Resources Association Middleburg VA : Assoc., 1967 33(1997), 3, Seite 0 Online-Ressource (DE-627)NLEJ243927428 (DE-600)2090051-X 1752-1688 nnns volume:33 year:1997 number:3 pages:0 http://dx.doi.org/10.1111/j.1752-1688.1997.tb03537.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 33 1997 3 0 |
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10.1111/j.1752-1688.1997.tb03537.x doi (DE-627)NLEJ243381662 DE-627 ger DE-627 rakwb Muttiah, Ranjan S. verfasserin aut PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS Oxford, UK Blackwell Publishing Ltd 1997 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier : The cascade correlation neural network was used to predict the two-year peak discharge (Q2) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q2 by regression. Our research shows Q2 was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds. 2007 Blackwell Publishing Journal Backfiles 1879-2005 |2007|||||||||| neural networks Srinivasan, Raghavan verfasserin aut Allen, Peter M. verfasserin aut In American Water Resources Association ; GKD-ID: 11654 Journal of the American Water Resources Association Middleburg VA : Assoc., 1967 33(1997), 3, Seite 0 Online-Ressource (DE-627)NLEJ243927428 (DE-600)2090051-X 1752-1688 nnns volume:33 year:1997 number:3 pages:0 http://dx.doi.org/10.1111/j.1752-1688.1997.tb03537.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 33 1997 3 0 |
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10.1111/j.1752-1688.1997.tb03537.x doi (DE-627)NLEJ243381662 DE-627 ger DE-627 rakwb Muttiah, Ranjan S. verfasserin aut PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS Oxford, UK Blackwell Publishing Ltd 1997 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier : The cascade correlation neural network was used to predict the two-year peak discharge (Q2) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q2 by regression. Our research shows Q2 was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds. 2007 Blackwell Publishing Journal Backfiles 1879-2005 |2007|||||||||| neural networks Srinivasan, Raghavan verfasserin aut Allen, Peter M. verfasserin aut In American Water Resources Association ; GKD-ID: 11654 Journal of the American Water Resources Association Middleburg VA : Assoc., 1967 33(1997), 3, Seite 0 Online-Ressource (DE-627)NLEJ243927428 (DE-600)2090051-X 1752-1688 nnns volume:33 year:1997 number:3 pages:0 http://dx.doi.org/10.1111/j.1752-1688.1997.tb03537.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 33 1997 3 0 |
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10.1111/j.1752-1688.1997.tb03537.x doi (DE-627)NLEJ243381662 DE-627 ger DE-627 rakwb Muttiah, Ranjan S. verfasserin aut PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS Oxford, UK Blackwell Publishing Ltd 1997 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier : The cascade correlation neural network was used to predict the two-year peak discharge (Q2) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q2 by regression. Our research shows Q2 was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds. 2007 Blackwell Publishing Journal Backfiles 1879-2005 |2007|||||||||| neural networks Srinivasan, Raghavan verfasserin aut Allen, Peter M. verfasserin aut In American Water Resources Association ; GKD-ID: 11654 Journal of the American Water Resources Association Middleburg VA : Assoc., 1967 33(1997), 3, Seite 0 Online-Ressource (DE-627)NLEJ243927428 (DE-600)2090051-X 1752-1688 nnns volume:33 year:1997 number:3 pages:0 http://dx.doi.org/10.1111/j.1752-1688.1997.tb03537.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 33 1997 3 0 |
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10.1111/j.1752-1688.1997.tb03537.x doi (DE-627)NLEJ243381662 DE-627 ger DE-627 rakwb Muttiah, Ranjan S. verfasserin aut PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS Oxford, UK Blackwell Publishing Ltd 1997 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier : The cascade correlation neural network was used to predict the two-year peak discharge (Q2) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q2 by regression. Our research shows Q2 was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds. 2007 Blackwell Publishing Journal Backfiles 1879-2005 |2007|||||||||| neural networks Srinivasan, Raghavan verfasserin aut Allen, Peter M. verfasserin aut In American Water Resources Association ; GKD-ID: 11654 Journal of the American Water Resources Association Middleburg VA : Assoc., 1967 33(1997), 3, Seite 0 Online-Ressource (DE-627)NLEJ243927428 (DE-600)2090051-X 1752-1688 nnns volume:33 year:1997 number:3 pages:0 http://dx.doi.org/10.1111/j.1752-1688.1997.tb03537.x text/html Verlag Deutschlandweit zugänglich Volltext GBV_USEFLAG_U ZDB-1-DJB GBV_NL_ARTICLE AR 33 1997 3 0 |
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PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS |
| abstract |
: The cascade correlation neural network was used to predict the two-year peak discharge (Q2) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q2 by regression. Our research shows Q2 was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds. |
| abstractGer |
: The cascade correlation neural network was used to predict the two-year peak discharge (Q2) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q2 by regression. Our research shows Q2 was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds. |
| abstract_unstemmed |
: The cascade correlation neural network was used to predict the two-year peak discharge (Q2) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q2 by regression. Our research shows Q2 was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ243381662</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210707180339.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">120427s1997 xx |||||o 00| ||und c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1111/j.1752-1688.1997.tb03537.x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ243381662</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Muttiah, Ranjan S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Oxford, UK</subfield><subfield code="b">Blackwell Publishing Ltd</subfield><subfield code="c">1997</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">: The cascade correlation neural network was used to predict the two-year peak discharge (Q2) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q2 by regression. Our research shows Q2 was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="d">2007</subfield><subfield code="f">Blackwell Publishing Journal Backfiles 1879-2005</subfield><subfield code="7">|2007||||||||||</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neural networks</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Srinivasan, Raghavan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Allen, Peter M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="a">American Water Resources Association ; GKD-ID: 11654</subfield><subfield code="t">Journal of the American Water Resources Association</subfield><subfield code="d">Middleburg VA : Assoc., 1967</subfield><subfield code="g">33(1997), 3, Seite 0</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ243927428</subfield><subfield code="w">(DE-600)2090051-X</subfield><subfield code="x">1752-1688</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:33</subfield><subfield code="g">year:1997</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://dx.doi.org/10.1111/j.1752-1688.1997.tb03537.x</subfield><subfield code="q">text/html</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-DJB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">33</subfield><subfield code="j">1997</subfield><subfield code="e">3</subfield><subfield code="h">0</subfield></datafield></record></collection>
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