A multiple-input single-output model for flow forecasting
A Multiple-input single-output time-invariant, non-linear model, based on a black-box systems approach, was used for flow forecasting during monsoon flood events using daily data. The model consists of a non-linear component representing the immediate and moderately delayed responses and a linear co...
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| Veröffentlicht in: | Journal of hydrology (Amsterdam) 1999-07, Vol.220 (1), p.12-26 |
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| container_title | Journal of hydrology (Amsterdam) |
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| creator | Kothyari, U.C. Singh, V.P. |
| description | A Multiple-input single-output time-invariant, non-linear model, based on a black-box systems approach, was used for flow forecasting during monsoon flood events using daily data. The model consists of a non-linear component representing the immediate and moderately delayed responses and a linear component representing the delayed response of the catchment. It is based on a structure originally proposed by Muftuoglu, R.F. (New models for nonlinear catchment analysis. J. Hydrol. 73 (1984) 335–357; Monthly runoff generation by non-linear models. J. Hydrol. 125 (1991) 277–291). The spatial variation in rainfall amounts is incorporated in the model by treating the rainfall as separate lumped inputs. The model, in its non-parametric form, is first calibrated on various data sets stacked together and then verification tests are performed on different data sets. The results show that the model is capable of forecasting flows with high efficiencies for the data used, indicating that it provides a low cost alternative model for use in sparse-data scenarios and for small-one-off projects. |
| doi_str_mv | 10.1016/S0022-1694(99)00055-4 |
| format | Article |
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The model consists of a non-linear component representing the immediate and moderately delayed responses and a linear component representing the delayed response of the catchment. It is based on a structure originally proposed by Muftuoglu, R.F. (New models for nonlinear catchment analysis. J. Hydrol. 73 (1984) 335–357; Monthly runoff generation by non-linear models. J. Hydrol. 125 (1991) 277–291). The spatial variation in rainfall amounts is incorporated in the model by treating the rainfall as separate lumped inputs. The model, in its non-parametric form, is first calibrated on various data sets stacked together and then verification tests are performed on different data sets. 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The model consists of a non-linear component representing the immediate and moderately delayed responses and a linear component representing the delayed response of the catchment. It is based on a structure originally proposed by Muftuoglu, R.F. (New models for nonlinear catchment analysis. J. Hydrol. 73 (1984) 335–357; Monthly runoff generation by non-linear models. J. Hydrol. 125 (1991) 277–291). The spatial variation in rainfall amounts is incorporated in the model by treating the rainfall as separate lumped inputs. The model, in its non-parametric form, is first calibrated on various data sets stacked together and then verification tests are performed on different data sets. The results show that the model is capable of forecasting flows with high efficiencies for the data used, indicating that it provides a low cost alternative model for use in sparse-data scenarios and for small-one-off projects.</description><subject>Catchment</subject><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Engineering and environment geology. Geothermics</subject><subject>Exact sciences and technology</subject><subject>Forecasting</subject><subject>Freshwater</subject><subject>Heterogeneity</subject><subject>Hydrology</subject><subject>Hydrology. Hydrogeology</subject><subject>mathematical models</subject><subject>Natural hazards: prediction, damages, etc</subject><subject>Rainfall</subject><subject>Response Function</subject><subject>Runoff</subject><subject>stream flow</subject><subject>watershed hydrology</subject><subject>watersheds</subject><issn>0022-1694</issn><issn>1879-2707</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><recordid>eNqF0E1PwyAYwHFiNHFOP4JxB2P0UOWhUMrJLItvyRIPc2fCKCyYrkxoNX576bro0ROQ_B4gf4TOAd8ChuJugTEhGRSCXgtxgzFmLKMHaAQlFxnhmB-i0S85RicxvieE85yOkJhONl3dum1tMtdsu3YSXbNOB9-1_WnjK1NPrA8TW_uvfmO0im0yp-jIqjqas_06RsvHh7fZczZ_fXqZTeeZokDbDIShlJaCVAACKspIUZFVCYZolVsurFqBYbQkmnAGXHBjc7uyRWWtAqhwPkZXw73b4D86E1u5cVGbulaN8V2UwPMCiBAJsgHq4GMMxsptcBsVviVg2YeSu1CyryCFkLtQkqa5y_0DKmpV26Aa7eLfcFkQWrDELgZmlZdqHRJZLgiGHBORM85IEveDMCnHpzNBRu1Mo03lUrVWVt7985UfI6aFJA</recordid><startdate>19990726</startdate><enddate>19990726</enddate><creator>Kothyari, U.C.</creator><creator>Singh, V.P.</creator><general>Elsevier B.V</general><general>Elsevier Science</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope></search><sort><creationdate>19990726</creationdate><title>A multiple-input single-output model for flow forecasting</title><author>Kothyari, U.C. ; Singh, V.P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a414t-19e444892d1191d4526d2b81e2ca3f79fab1e5482c2751797ef3fbf6dffa11d03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Catchment</topic><topic>Earth sciences</topic><topic>Earth, ocean, space</topic><topic>Engineering and environment geology. Geothermics</topic><topic>Exact sciences and technology</topic><topic>Forecasting</topic><topic>Freshwater</topic><topic>Heterogeneity</topic><topic>Hydrology</topic><topic>Hydrology. Hydrogeology</topic><topic>mathematical models</topic><topic>Natural hazards: prediction, damages, etc</topic><topic>Rainfall</topic><topic>Response Function</topic><topic>Runoff</topic><topic>stream flow</topic><topic>watershed hydrology</topic><topic>watersheds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kothyari, U.C.</creatorcontrib><creatorcontrib>Singh, V.P.</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Journal of hydrology (Amsterdam)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kothyari, U.C.</au><au>Singh, V.P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A multiple-input single-output model for flow forecasting</atitle><jtitle>Journal of hydrology (Amsterdam)</jtitle><date>1999-07-26</date><risdate>1999</risdate><volume>220</volume><issue>1</issue><spage>12</spage><epage>26</epage><pages>12-26</pages><issn>0022-1694</issn><eissn>1879-2707</eissn><coden>JHYDA7</coden><abstract>A Multiple-input single-output time-invariant, non-linear model, based on a black-box systems approach, was used for flow forecasting during monsoon flood events using daily data. The model consists of a non-linear component representing the immediate and moderately delayed responses and a linear component representing the delayed response of the catchment. It is based on a structure originally proposed by Muftuoglu, R.F. (New models for nonlinear catchment analysis. J. Hydrol. 73 (1984) 335–357; Monthly runoff generation by non-linear models. J. Hydrol. 125 (1991) 277–291). The spatial variation in rainfall amounts is incorporated in the model by treating the rainfall as separate lumped inputs. The model, in its non-parametric form, is first calibrated on various data sets stacked together and then verification tests are performed on different data sets. The results show that the model is capable of forecasting flows with high efficiencies for the data used, indicating that it provides a low cost alternative model for use in sparse-data scenarios and for small-one-off projects.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/S0022-1694(99)00055-4</doi><tpages>15</tpages></addata></record> |
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| ispartof | Journal of hydrology (Amsterdam), 1999-07, Vol.220 (1), p.12-26 |
| issn | 0022-1694 1879-2707 |
| language | eng |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Catchment Earth sciences Earth, ocean, space Engineering and environment geology. Geothermics Exact sciences and technology Forecasting Freshwater Heterogeneity Hydrology Hydrology. Hydrogeology mathematical models Natural hazards: prediction, damages, etc Rainfall Response Function Runoff stream flow watershed hydrology watersheds |
| title | A multiple-input single-output model for flow forecasting |
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