Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model

Abstract In the lower Sevier River basin in Utah, the travel times between reservoir releases and arrival at irrigation canal diversions limit the reservoir operation in enabling delivery changes, which may not be compatible with the on demand schedule in the basin. This research presents a robust m...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ticlavilca, Andres M. [verfasserIn] McKee, Mac Walker, Wynn R.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Irrigation Canal Irrigation Season Relevance Vector Machine Irrigation Demand DMAD

Anmerkung:	© Springer-Verlag 2011

Übergeordnetes Werk:	Enthalten in: Irrigation science - Berlin : Springer, 1978, 31(2011), 2 vom: 13. Aug., Seite 151-167
Übergeordnetes Werk:	volume:31 ; year:2011 ; number:2 ; day:13 ; month:08 ; pages:151-167

Links:	Volltext

DOI / URN:	10.1007/s00271-011-0300-6

Katalog-ID:	SPR003540510

Internformat


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520			\|a Abstract In the lower Sevier River basin in Utah, the travel times between reservoir releases and arrival at irrigation canal diversions limit the reservoir operation in enabling delivery changes, which may not be compatible with the on demand schedule in the basin. This research presents a robust machine learning approach to forecast the short-term diversion demands for three irrigation canals. These real-time predictions can assist the operator to react promptly to short-term changes in demand and to properly release water from the reservoir. The models are developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a Bayesian learning machine approach for regression. Predictive confidence intervals can also be obtained from the model with this Bayesian approach. Test results show that the MVRVM learns the input–output patterns with good accuracy. A bootstrap analysis is used to evaluate robustness of model parameter estimation. The MVRVM is compared in terms of performance and robustness with an Artificial Neural Network.
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allfields	10.1007/s00271-011-0300-6 doi (DE-627)SPR003540510 (SPR)s00271-011-0300-6-e DE-627 ger DE-627 rakwb eng Ticlavilca, Andres M. verfasserin aut Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract In the lower Sevier River basin in Utah, the travel times between reservoir releases and arrival at irrigation canal diversions limit the reservoir operation in enabling delivery changes, which may not be compatible with the on demand schedule in the basin. This research presents a robust machine learning approach to forecast the short-term diversion demands for three irrigation canals. These real-time predictions can assist the operator to react promptly to short-term changes in demand and to properly release water from the reservoir. The models are developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a Bayesian learning machine approach for regression. Predictive confidence intervals can also be obtained from the model with this Bayesian approach. Test results show that the MVRVM learns the input–output patterns with good accuracy. A bootstrap analysis is used to evaluate robustness of model parameter estimation. The MVRVM is compared in terms of performance and robustness with an Artificial Neural Network. Irrigation Canal (dpeaa)DE-He213 Irrigation Season (dpeaa)DE-He213 Relevance Vector Machine (dpeaa)DE-He213 Irrigation Demand (dpeaa)DE-He213 DMAD (dpeaa)DE-He213 McKee, Mac aut Walker, Wynn R. aut Enthalten in Irrigation science Berlin : Springer, 1978 31(2011), 2 vom: 13. Aug., Seite 151-167 (DE-627)271175362 (DE-600)1478936-X 1432-1319 nnns volume:31 year:2011 number:2 day:13 month:08 pages:151-167 https://dx.doi.org/10.1007/s00271-011-0300-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2011 2 13 08 151-167
spelling	10.1007/s00271-011-0300-6 doi (DE-627)SPR003540510 (SPR)s00271-011-0300-6-e DE-627 ger DE-627 rakwb eng Ticlavilca, Andres M. verfasserin aut Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract In the lower Sevier River basin in Utah, the travel times between reservoir releases and arrival at irrigation canal diversions limit the reservoir operation in enabling delivery changes, which may not be compatible with the on demand schedule in the basin. This research presents a robust machine learning approach to forecast the short-term diversion demands for three irrigation canals. These real-time predictions can assist the operator to react promptly to short-term changes in demand and to properly release water from the reservoir. The models are developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a Bayesian learning machine approach for regression. Predictive confidence intervals can also be obtained from the model with this Bayesian approach. Test results show that the MVRVM learns the input–output patterns with good accuracy. A bootstrap analysis is used to evaluate robustness of model parameter estimation. The MVRVM is compared in terms of performance and robustness with an Artificial Neural Network. Irrigation Canal (dpeaa)DE-He213 Irrigation Season (dpeaa)DE-He213 Relevance Vector Machine (dpeaa)DE-He213 Irrigation Demand (dpeaa)DE-He213 DMAD (dpeaa)DE-He213 McKee, Mac aut Walker, Wynn R. aut Enthalten in Irrigation science Berlin : Springer, 1978 31(2011), 2 vom: 13. Aug., Seite 151-167 (DE-627)271175362 (DE-600)1478936-X 1432-1319 nnns volume:31 year:2011 number:2 day:13 month:08 pages:151-167 https://dx.doi.org/10.1007/s00271-011-0300-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2011 2 13 08 151-167
allfields_unstemmed	10.1007/s00271-011-0300-6 doi (DE-627)SPR003540510 (SPR)s00271-011-0300-6-e DE-627 ger DE-627 rakwb eng Ticlavilca, Andres M. verfasserin aut Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract In the lower Sevier River basin in Utah, the travel times between reservoir releases and arrival at irrigation canal diversions limit the reservoir operation in enabling delivery changes, which may not be compatible with the on demand schedule in the basin. This research presents a robust machine learning approach to forecast the short-term diversion demands for three irrigation canals. These real-time predictions can assist the operator to react promptly to short-term changes in demand and to properly release water from the reservoir. The models are developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a Bayesian learning machine approach for regression. Predictive confidence intervals can also be obtained from the model with this Bayesian approach. Test results show that the MVRVM learns the input–output patterns with good accuracy. A bootstrap analysis is used to evaluate robustness of model parameter estimation. The MVRVM is compared in terms of performance and robustness with an Artificial Neural Network. Irrigation Canal (dpeaa)DE-He213 Irrigation Season (dpeaa)DE-He213 Relevance Vector Machine (dpeaa)DE-He213 Irrigation Demand (dpeaa)DE-He213 DMAD (dpeaa)DE-He213 McKee, Mac aut Walker, Wynn R. aut Enthalten in Irrigation science Berlin : Springer, 1978 31(2011), 2 vom: 13. Aug., Seite 151-167 (DE-627)271175362 (DE-600)1478936-X 1432-1319 nnns volume:31 year:2011 number:2 day:13 month:08 pages:151-167 https://dx.doi.org/10.1007/s00271-011-0300-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2011 2 13 08 151-167
allfieldsGer	10.1007/s00271-011-0300-6 doi (DE-627)SPR003540510 (SPR)s00271-011-0300-6-e DE-627 ger DE-627 rakwb eng Ticlavilca, Andres M. verfasserin aut Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract In the lower Sevier River basin in Utah, the travel times between reservoir releases and arrival at irrigation canal diversions limit the reservoir operation in enabling delivery changes, which may not be compatible with the on demand schedule in the basin. This research presents a robust machine learning approach to forecast the short-term diversion demands for three irrigation canals. These real-time predictions can assist the operator to react promptly to short-term changes in demand and to properly release water from the reservoir. The models are developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a Bayesian learning machine approach for regression. Predictive confidence intervals can also be obtained from the model with this Bayesian approach. Test results show that the MVRVM learns the input–output patterns with good accuracy. A bootstrap analysis is used to evaluate robustness of model parameter estimation. The MVRVM is compared in terms of performance and robustness with an Artificial Neural Network. Irrigation Canal (dpeaa)DE-He213 Irrigation Season (dpeaa)DE-He213 Relevance Vector Machine (dpeaa)DE-He213 Irrigation Demand (dpeaa)DE-He213 DMAD (dpeaa)DE-He213 McKee, Mac aut Walker, Wynn R. aut Enthalten in Irrigation science Berlin : Springer, 1978 31(2011), 2 vom: 13. Aug., Seite 151-167 (DE-627)271175362 (DE-600)1478936-X 1432-1319 nnns volume:31 year:2011 number:2 day:13 month:08 pages:151-167 https://dx.doi.org/10.1007/s00271-011-0300-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2011 2 13 08 151-167
allfieldsSound	10.1007/s00271-011-0300-6 doi (DE-627)SPR003540510 (SPR)s00271-011-0300-6-e DE-627 ger DE-627 rakwb eng Ticlavilca, Andres M. verfasserin aut Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract In the lower Sevier River basin in Utah, the travel times between reservoir releases and arrival at irrigation canal diversions limit the reservoir operation in enabling delivery changes, which may not be compatible with the on demand schedule in the basin. This research presents a robust machine learning approach to forecast the short-term diversion demands for three irrigation canals. These real-time predictions can assist the operator to react promptly to short-term changes in demand and to properly release water from the reservoir. The models are developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a Bayesian learning machine approach for regression. Predictive confidence intervals can also be obtained from the model with this Bayesian approach. Test results show that the MVRVM learns the input–output patterns with good accuracy. A bootstrap analysis is used to evaluate robustness of model parameter estimation. The MVRVM is compared in terms of performance and robustness with an Artificial Neural Network. Irrigation Canal (dpeaa)DE-He213 Irrigation Season (dpeaa)DE-He213 Relevance Vector Machine (dpeaa)DE-He213 Irrigation Demand (dpeaa)DE-He213 DMAD (dpeaa)DE-He213 McKee, Mac aut Walker, Wynn R. aut Enthalten in Irrigation science Berlin : Springer, 1978 31(2011), 2 vom: 13. Aug., Seite 151-167 (DE-627)271175362 (DE-600)1478936-X 1432-1319 nnns volume:31 year:2011 number:2 day:13 month:08 pages:151-167 https://dx.doi.org/10.1007/s00271-011-0300-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 31 2011 2 13 08 151-167
language	English
source	Enthalten in Irrigation science 31(2011), 2 vom: 13. Aug., Seite 151-167 volume:31 year:2011 number:2 day:13 month:08 pages:151-167
sourceStr	Enthalten in Irrigation science 31(2011), 2 vom: 13. Aug., Seite 151-167 volume:31 year:2011 number:2 day:13 month:08 pages:151-167
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Irrigation Canal Irrigation Season Relevance Vector Machine Irrigation Demand DMAD
isfreeaccess_bool	false
container_title	Irrigation science
authorswithroles_txt_mv	Ticlavilca, Andres M. @@aut@@ McKee, Mac @@aut@@ Walker, Wynn R. @@aut@@
publishDateDaySort_date	2011-08-13T00:00:00Z
hierarchy_top_id	271175362
id	SPR003540510
language_de	englisch
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author	Ticlavilca, Andres M.
spellingShingle	Ticlavilca, Andres M. misc Irrigation Canal misc Irrigation Season misc Relevance Vector Machine misc Irrigation Demand misc DMAD Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model
authorStr	Ticlavilca, Andres M.
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topic_title	Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model Irrigation Canal (dpeaa)DE-He213 Irrigation Season (dpeaa)DE-He213 Relevance Vector Machine (dpeaa)DE-He213 Irrigation Demand (dpeaa)DE-He213 DMAD (dpeaa)DE-He213
topic	misc Irrigation Canal misc Irrigation Season misc Relevance Vector Machine misc Irrigation Demand misc DMAD
topic_unstemmed	misc Irrigation Canal misc Irrigation Season misc Relevance Vector Machine misc Irrigation Demand misc DMAD
topic_browse	misc Irrigation Canal misc Irrigation Season misc Relevance Vector Machine misc Irrigation Demand misc DMAD
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title	Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model
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title_full	Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model
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author_browse	Ticlavilca, Andres M. McKee, Mac Walker, Wynn R.
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doi_str_mv	10.1007/s00271-011-0300-6
title_sort	real-time forecasting of short-term irrigation canal demands using a robust multivariate bayesian learning model
title_auth	Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model
abstract	Abstract In the lower Sevier River basin in Utah, the travel times between reservoir releases and arrival at irrigation canal diversions limit the reservoir operation in enabling delivery changes, which may not be compatible with the on demand schedule in the basin. This research presents a robust machine learning approach to forecast the short-term diversion demands for three irrigation canals. These real-time predictions can assist the operator to react promptly to short-term changes in demand and to properly release water from the reservoir. The models are developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a Bayesian learning machine approach for regression. Predictive confidence intervals can also be obtained from the model with this Bayesian approach. Test results show that the MVRVM learns the input–output patterns with good accuracy. A bootstrap analysis is used to evaluate robustness of model parameter estimation. The MVRVM is compared in terms of performance and robustness with an Artificial Neural Network. © Springer-Verlag 2011
abstractGer	Abstract In the lower Sevier River basin in Utah, the travel times between reservoir releases and arrival at irrigation canal diversions limit the reservoir operation in enabling delivery changes, which may not be compatible with the on demand schedule in the basin. This research presents a robust machine learning approach to forecast the short-term diversion demands for three irrigation canals. These real-time predictions can assist the operator to react promptly to short-term changes in demand and to properly release water from the reservoir. The models are developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a Bayesian learning machine approach for regression. Predictive confidence intervals can also be obtained from the model with this Bayesian approach. Test results show that the MVRVM learns the input–output patterns with good accuracy. A bootstrap analysis is used to evaluate robustness of model parameter estimation. The MVRVM is compared in terms of performance and robustness with an Artificial Neural Network. © Springer-Verlag 2011
abstract_unstemmed	Abstract In the lower Sevier River basin in Utah, the travel times between reservoir releases and arrival at irrigation canal diversions limit the reservoir operation in enabling delivery changes, which may not be compatible with the on demand schedule in the basin. This research presents a robust machine learning approach to forecast the short-term diversion demands for three irrigation canals. These real-time predictions can assist the operator to react promptly to short-term changes in demand and to properly release water from the reservoir. The models are developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a Bayesian learning machine approach for regression. Predictive confidence intervals can also be obtained from the model with this Bayesian approach. Test results show that the MVRVM learns the input–output patterns with good accuracy. A bootstrap analysis is used to evaluate robustness of model parameter estimation. The MVRVM is compared in terms of performance and robustness with an Artificial Neural Network. © Springer-Verlag 2011
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title_short	Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model
url	https://dx.doi.org/10.1007/s00271-011-0300-6
remote_bool	true
author2	McKee, Mac Walker, Wynn R.
author2Str	McKee, Mac Walker, Wynn R.
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doi_str	10.1007/s00271-011-0300-6
up_date	2024-07-03T20:07:34.431Z
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Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model

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