Forecasting daily potential evapotranspiration using machine learning and limited climatic data
▶ Forecasting ET o models for water managing purposes (1985 Hargreaves ET o). ▶ 2 ET o forecasting approaches using air temperature and Multivariate Relevance Vector Machine. ▶ Ind. Approach provides better and larger forecast lags than Dir. Approach. ▶ Better ET o forecast when using forecasted wea...
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description | ▶ Forecasting ET
o models for water managing purposes (1985 Hargreaves ET
o). ▶ 2 ET
o forecasting approaches using air temperature and Multivariate Relevance Vector Machine. ▶ Ind. Approach provides better and larger forecast lags than Dir. Approach. ▶ Better ET
o forecast when using forecasted weather variables rather than ET
o values. ▶ In study case, ET
o is forecasted of up to 4 days using the Ind. Approach.
Anticipating, or forecasting near-term irrigation demands is a requirement for improved management of conveyance and delivery systems. The most important component of a forecasting regime for irrigation is a simple, yet reliable, approach for forecasting crop water demands, which in this paper is represented by the reference or potential evapotranspiration (ET
o). In most cases, weather data in the area is limited to a reduced number of variables measured, therefore current or future ET
o estimation is restricted. This paper summarizes the results of testing of two proposed forecasting ET
o schemes under the mentioned conditions. The first or “direct” approach involved forecasting ET
o using historically computed ET
o values. The second or “indirect” approach involved forecasting the required weather parameters for the ET
o calculation based on historical data and then computing ET
o. An statistical machine learning algorithm, the Multivariate Relevance Vector Machine (MVRVM) is applied to both of the forecastings schemes. The general ET
o model used is the 1985 Hargreaves Equation which requires only minimum and maximum daily air temperatures and is thus well suited to regions lacking more comprehensive climatic data. The utility and practicality of the forecasting methodology is demonstrated with an application to an irrigation project in Central Utah. To determine the advantage and suitability of the applied algorithm, another learning machine, the Multilayer Perceptron (MLP), is used for comparison purposes. The robustness and stability of the proposed schemes are tested by the application of the bootstrap analysis. |
doi_str_mv | 10.1016/j.agwat.2010.10.012 |
format | Article |
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o models for water managing purposes (1985 Hargreaves ET
o). ▶ 2 ET
o forecasting approaches using air temperature and Multivariate Relevance Vector Machine. ▶ Ind. Approach provides better and larger forecast lags than Dir. Approach. ▶ Better ET
o forecast when using forecasted weather variables rather than ET
o values. ▶ In study case, ET
o is forecasted of up to 4 days using the Ind. Approach.
Anticipating, or forecasting near-term irrigation demands is a requirement for improved management of conveyance and delivery systems. The most important component of a forecasting regime for irrigation is a simple, yet reliable, approach for forecasting crop water demands, which in this paper is represented by the reference or potential evapotranspiration (ET
o). In most cases, weather data in the area is limited to a reduced number of variables measured, therefore current or future ET
o estimation is restricted. This paper summarizes the results of testing of two proposed forecasting ET
o schemes under the mentioned conditions. The first or “direct” approach involved forecasting ET
o using historically computed ET
o values. The second or “indirect” approach involved forecasting the required weather parameters for the ET
o calculation based on historical data and then computing ET
o. An statistical machine learning algorithm, the Multivariate Relevance Vector Machine (MVRVM) is applied to both of the forecastings schemes. The general ET
o model used is the 1985 Hargreaves Equation which requires only minimum and maximum daily air temperatures and is thus well suited to regions lacking more comprehensive climatic data. The utility and practicality of the forecasting methodology is demonstrated with an application to an irrigation project in Central Utah. To determine the advantage and suitability of the applied algorithm, another learning machine, the Multilayer Perceptron (MLP), is used for comparison purposes. The robustness and stability of the proposed schemes are tested by the application of the bootstrap analysis.</description><identifier>ISSN: 0378-3774</identifier><identifier>EISSN: 1873-2283</identifier><identifier>DOI: 10.1016/j.agwat.2010.10.012</identifier><identifier>CODEN: AWMADF</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Agricultural and forest climatology and meteorology. Irrigation. Drainage ; agricultural machinery and equipment ; Agronomy. Soil science and plant productions ; Algorithms ; automatic detection ; Biological and medical sciences ; Canal systems ; climatic factors ; Climatology ; daily potential evapotranspiration forecasting ; data analysis ; Demand ; Evapotranspiration ; Evapotranspiration Forecasting Hargreaves ETo equation Irrigation Canal systems Water management Machine learning ; Forecasting ; Fundamental and applied biological sciences. Psychology ; General agronomy. Plant production ; Hargreaves ET o equation ; Irrigation ; irrigation management ; Machine learning ; Mathematical analysis ; Mathematical models ; prediction ; Water management ; water requirement ; Weather</subject><ispartof>Agricultural water management, 2011-02, Vol.98 (4), p.553-562</ispartof><rights>2010 Elsevier B.V.</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c456t-1657860c4ad6a57d48999025ff08023f82e7264258f4ecd15ca13ea52e740f803</citedby><cites>FETCH-LOGICAL-c456t-1657860c4ad6a57d48999025ff08023f82e7264258f4ecd15ca13ea52e740f803</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.agwat.2010.10.012$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,781,785,3551,4009,27929,27930,46000</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23823425$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttp://econpapers.repec.org/article/eeeagiwat/v_3a98_3ay_3a2011_3ai_3a4_3ap_3a553-562.htm$$DView record in RePEc$$Hfree_for_read</backlink></links><search><creatorcontrib>Torres, Alfonso F.</creatorcontrib><creatorcontrib>Walker, Wynn R.</creatorcontrib><creatorcontrib>McKee, Mac</creatorcontrib><title>Forecasting daily potential evapotranspiration using machine learning and limited climatic data</title><title>Agricultural water management</title><description>▶ Forecasting ET
o models for water managing purposes (1985 Hargreaves ET
o). ▶ 2 ET
o forecasting approaches using air temperature and Multivariate Relevance Vector Machine. ▶ Ind. Approach provides better and larger forecast lags than Dir. Approach. ▶ Better ET
o forecast when using forecasted weather variables rather than ET
o values. ▶ In study case, ET
o is forecasted of up to 4 days using the Ind. Approach.
Anticipating, or forecasting near-term irrigation demands is a requirement for improved management of conveyance and delivery systems. The most important component of a forecasting regime for irrigation is a simple, yet reliable, approach for forecasting crop water demands, which in this paper is represented by the reference or potential evapotranspiration (ET
o). In most cases, weather data in the area is limited to a reduced number of variables measured, therefore current or future ET
o estimation is restricted. This paper summarizes the results of testing of two proposed forecasting ET
o schemes under the mentioned conditions. The first or “direct” approach involved forecasting ET
o using historically computed ET
o values. The second or “indirect” approach involved forecasting the required weather parameters for the ET
o calculation based on historical data and then computing ET
o. An statistical machine learning algorithm, the Multivariate Relevance Vector Machine (MVRVM) is applied to both of the forecastings schemes. The general ET
o model used is the 1985 Hargreaves Equation which requires only minimum and maximum daily air temperatures and is thus well suited to regions lacking more comprehensive climatic data. The utility and practicality of the forecasting methodology is demonstrated with an application to an irrigation project in Central Utah. To determine the advantage and suitability of the applied algorithm, another learning machine, the Multilayer Perceptron (MLP), is used for comparison purposes. The robustness and stability of the proposed schemes are tested by the application of the bootstrap analysis.</description><subject>Agricultural and forest climatology and meteorology. Irrigation. Drainage</subject><subject>agricultural machinery and equipment</subject><subject>Agronomy. Soil science and plant productions</subject><subject>Algorithms</subject><subject>automatic detection</subject><subject>Biological and medical sciences</subject><subject>Canal systems</subject><subject>climatic factors</subject><subject>Climatology</subject><subject>daily potential evapotranspiration forecasting</subject><subject>data analysis</subject><subject>Demand</subject><subject>Evapotranspiration</subject><subject>Evapotranspiration Forecasting Hargreaves ETo equation Irrigation Canal systems Water management Machine learning</subject><subject>Forecasting</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General agronomy. Plant production</subject><subject>Hargreaves ET o equation</subject><subject>Irrigation</subject><subject>irrigation management</subject><subject>Machine learning</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>prediction</subject><subject>Water management</subject><subject>water requirement</subject><subject>Weather</subject><issn>0378-3774</issn><issn>1873-2283</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNp9kU1v1DAQhiMEEkvLL-BALohTFn8mzoEDqmgLqtQD9GyNnPHWq6wTbO-i_fdMmqpHDuOxR8-8mnldVR8423LG2y_7Lez-QtkK9lTZMi5eVRtuOtkIYeTrasNkZxrZdept9S7nPWNMMdVtKns9JXSQS4i7eoAwnut5KhhLgLHGE9AjQcxzSFDCFOtjXsADuMcQsR4RUlwKEId6DIdQcKgdXQh2JFfgsnrjYcz4_jlfVA_X339f3TZ39zc_rr7dNU7ptjS81Z1pmVMwtKC7QZm-75nQ3jPDhPRGYCdaJbTxCt3AtQMuETSVFfOGyYvq86o7p-nPEXOxh5AdjiNEnI7ZkviyvRJEypV0aco5obdzooHT2XJmFzft3j65aRc3lyK5SV0_166EM7qXFkSEXVjgk5XQGzrOFNTJKQUKRTFTaC2tboV9LAcS-_Q8LGQHoyeHXcgvokIaIWlX4j6unIeJZkrEPPwibcl4L4xQPRFfVwLJ21PAZLMLGB0Ogb612GEK_13rHwzyrqc</recordid><startdate>20110201</startdate><enddate>20110201</enddate><creator>Torres, Alfonso F.</creator><creator>Walker, Wynn R.</creator><creator>McKee, Mac</creator><general>Elsevier B.V</general><general>Amsterdam; New York: Elsevier</general><general>Elsevier</general><scope>FBQ</scope><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>20110201</creationdate><title>Forecasting daily potential evapotranspiration using machine learning and limited climatic data</title><author>Torres, Alfonso F. ; Walker, Wynn R. ; McKee, Mac</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c456t-1657860c4ad6a57d48999025ff08023f82e7264258f4ecd15ca13ea52e740f803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Agricultural and forest climatology and meteorology. Irrigation. Drainage</topic><topic>agricultural machinery and equipment</topic><topic>Agronomy. Soil science and plant productions</topic><topic>Algorithms</topic><topic>automatic detection</topic><topic>Biological and medical sciences</topic><topic>Canal systems</topic><topic>climatic factors</topic><topic>Climatology</topic><topic>daily potential evapotranspiration forecasting</topic><topic>data analysis</topic><topic>Demand</topic><topic>Evapotranspiration</topic><topic>Evapotranspiration Forecasting Hargreaves ETo equation Irrigation Canal systems Water management Machine learning</topic><topic>Forecasting</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General agronomy. Plant production</topic><topic>Hargreaves ET o equation</topic><topic>Irrigation</topic><topic>irrigation management</topic><topic>Machine learning</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>prediction</topic><topic>Water management</topic><topic>water requirement</topic><topic>Weather</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Torres, Alfonso F.</creatorcontrib><creatorcontrib>Walker, Wynn R.</creatorcontrib><creatorcontrib>McKee, Mac</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Agricultural water management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Torres, Alfonso F.</au><au>Walker, Wynn R.</au><au>McKee, Mac</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Forecasting daily potential evapotranspiration using machine learning and limited climatic data</atitle><jtitle>Agricultural water management</jtitle><date>2011-02-01</date><risdate>2011</risdate><volume>98</volume><issue>4</issue><spage>553</spage><epage>562</epage><pages>553-562</pages><issn>0378-3774</issn><eissn>1873-2283</eissn><coden>AWMADF</coden><abstract>▶ Forecasting ET
o models for water managing purposes (1985 Hargreaves ET
o). ▶ 2 ET
o forecasting approaches using air temperature and Multivariate Relevance Vector Machine. ▶ Ind. Approach provides better and larger forecast lags than Dir. Approach. ▶ Better ET
o forecast when using forecasted weather variables rather than ET
o values. ▶ In study case, ET
o is forecasted of up to 4 days using the Ind. Approach.
Anticipating, or forecasting near-term irrigation demands is a requirement for improved management of conveyance and delivery systems. The most important component of a forecasting regime for irrigation is a simple, yet reliable, approach for forecasting crop water demands, which in this paper is represented by the reference or potential evapotranspiration (ET
o). In most cases, weather data in the area is limited to a reduced number of variables measured, therefore current or future ET
o estimation is restricted. This paper summarizes the results of testing of two proposed forecasting ET
o schemes under the mentioned conditions. The first or “direct” approach involved forecasting ET
o using historically computed ET
o values. The second or “indirect” approach involved forecasting the required weather parameters for the ET
o calculation based on historical data and then computing ET
o. An statistical machine learning algorithm, the Multivariate Relevance Vector Machine (MVRVM) is applied to both of the forecastings schemes. The general ET
o model used is the 1985 Hargreaves Equation which requires only minimum and maximum daily air temperatures and is thus well suited to regions lacking more comprehensive climatic data. The utility and practicality of the forecasting methodology is demonstrated with an application to an irrigation project in Central Utah. To determine the advantage and suitability of the applied algorithm, another learning machine, the Multilayer Perceptron (MLP), is used for comparison purposes. The robustness and stability of the proposed schemes are tested by the application of the bootstrap analysis.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.agwat.2010.10.012</doi><tpages>10</tpages></addata></record> |
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subjects | Agricultural and forest climatology and meteorology. Irrigation. Drainage agricultural machinery and equipment Agronomy. Soil science and plant productions Algorithms automatic detection Biological and medical sciences Canal systems climatic factors Climatology daily potential evapotranspiration forecasting data analysis Demand Evapotranspiration Evapotranspiration Forecasting Hargreaves ETo equation Irrigation Canal systems Water management Machine learning Forecasting Fundamental and applied biological sciences. Psychology General agronomy. Plant production Hargreaves ET o equation Irrigation irrigation management Machine learning Mathematical analysis Mathematical models prediction Water management water requirement Weather |
title | Forecasting daily potential evapotranspiration using machine learning and limited climatic data |
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