The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China
The accurate prediction of reference crop evapotranspiration is of great significance to climate research and regional agricultural water management. In order to realize the high-precision prediction of ETO in the absence of meteorological data, this study use XGBoost to select key influencing facto...
Gespeichert in:
| Veröffentlicht in: | PloS one 2022-06, Vol.17 (6), p.e0269746-e0269746 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e0269746 |
|---|---|
| container_issue | 6 |
| container_start_page | e0269746 |
| container_title | PloS one |
| container_volume | 17 |
| creator | Zhao, Zhenhua Feng, Guohua Zhang, Jing |
| description | The accurate prediction of reference crop evapotranspiration is of great significance to climate research and regional agricultural water management. In order to realize the high-precision prediction of ETO in the absence of meteorological data, this study use XGBoost to select key influencing factors and BP algorithm to construct ETO prediction model of 12 meteorological stations in South West China in this study. ACO, CSO and CS algorithms are used to optimize the model and improve the adaptability of the model. The results show that Tmax, n and Ra can be used as the input combination of ETO model construction, and Tmax is the primary factor affecting ETO. ETO model constructed by BP algorithm has good goodness of fit with the ETO calculated by FAO-56 PM and ACO, CSO and CS have significant optimization effect on BP algorithm, among which CSO algorithm has the best optimization ability on BP, with RMSE, R2, MAE, NSE, GPI ranging 0.200-0.377, 0.932-0.984, 0.140-0.261, 0.920-0.984, 1.472-2.000, GPI ranking is 1-23. Therefore, the input combination (Tmax, n and Ra) and CSO-BP model are recommended as a simplified model for ETO prediction in Southwest China. |
| doi_str_mv | 10.1371/journal.pone.0269746 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2686268182</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A707006875</galeid><doaj_id>oai_doaj_org_article_164e4272d18d4e74bdb61792204d0963</doaj_id><sourcerecordid>A707006875</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-77cd5529c13ac6c7dd3673ac57bb1df6541e7d32e88dfe0b36a89fa55052a5e43</originalsourceid><addsrcrecordid>eNqNk12L1DAUhoso7rr6D0QDgujFjPlok_ZGWAc_BhZW3NXbkCan04ydppu0q_vvTZ3uMpW9kFIaTp_3bfPmnCR5TvCSMEHebd3gW9UsO9fCElNeiJQ_SI5JweiCU8weHqyPkichbDHOWM754-SIZbzgKWbHyc_LGlCwu66xlQWD6pvSW4N2zkCDShViybXow1fUO9R5MFb3qI8SDxV4aDUg7V2H4Fp1rveqDZ31qrdRY1t04Ya-_gWhR6vatupp8qhSTYBn0_Mk-f7p4-Xqy-Ls_PN6dXq20Lyg_UIIbbKMFpowpbkWxjAu4jITZUlMxbOUgDCMQp6bCnDJuMqLSmUZzqjKIGUnycu9b9e4IKecgqQ85_EmOY3Eek8Yp7ay83an_I10ysq_Bec3Uvne6gYk4SmkVFBDcpOCSEtTciIKSnFqcMFZ9Ho_fW0od2A0tDGHZmY6f9PaWm7ctSxIQQQV0eDNZODd1RDTkjsbNDSNasEN43-LrMhphCP66h_0_t1N1EbFDdi2Gk9Gj6byVGCBMc9FFqnlPVS8DOysjk1V2VifCd7OBJHp4Xe_UUMIcn3x7f_Z8x9z9vUBW4Nq-jq4Zhi7KMzBdA_GlgshduBdyATLcSZu05DjTMhpJqLsxeEB3Yluh4D9AXO7BtA</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2686268182</pqid></control><display><type>article</type><title>The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Public Library of Science (PLoS)</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Zhao, Zhenhua ; Feng, Guohua ; Zhang, Jing</creator><contributor>Mohammadzadeh, Ardashir</contributor><creatorcontrib>Zhao, Zhenhua ; Feng, Guohua ; Zhang, Jing ; Mohammadzadeh, Ardashir</creatorcontrib><description>The accurate prediction of reference crop evapotranspiration is of great significance to climate research and regional agricultural water management. In order to realize the high-precision prediction of ETO in the absence of meteorological data, this study use XGBoost to select key influencing factors and BP algorithm to construct ETO prediction model of 12 meteorological stations in South West China in this study. ACO, CSO and CS algorithms are used to optimize the model and improve the adaptability of the model. The results show that Tmax, n and Ra can be used as the input combination of ETO model construction, and Tmax is the primary factor affecting ETO. ETO model constructed by BP algorithm has good goodness of fit with the ETO calculated by FAO-56 PM and ACO, CSO and CS have significant optimization effect on BP algorithm, among which CSO algorithm has the best optimization ability on BP, with RMSE, R2, MAE, NSE, GPI ranging 0.200-0.377, 0.932-0.984, 0.140-0.261, 0.920-0.984, 1.472-2.000, GPI ranking is 1-23. Therefore, the input combination (Tmax, n and Ra) and CSO-BP model are recommended as a simplified model for ETO prediction in Southwest China.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0269746</identifier><identifier>PMID: 35696403</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Adaptability ; Agricultural management ; Agricultural research ; Agriculture ; Algorithms ; Analysis ; Ant colony optimization ; Biology and Life Sciences ; China ; Climate ; Climate change ; Computer and Information Sciences ; Crop evapotranspiration ; Earth Sciences ; Evapotranspiration ; Goodness of fit ; Humidity ; Hydrologic cycle ; Machine learning ; Meteorological data ; Meteorology ; Modelling ; Neural networks ; Neurons ; Optimization ; Optimization algorithms ; Physical Sciences ; Precipitation ; Prediction models ; Propagation ; Radiation ; Regional climates ; Research and Analysis Methods ; Social Sciences ; Support vector machines ; Water management ; Weather stations ; Wind</subject><ispartof>PloS one, 2022-06, Vol.17 (6), p.e0269746-e0269746</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Zhao et al 2022 Zhao et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-77cd5529c13ac6c7dd3673ac57bb1df6541e7d32e88dfe0b36a89fa55052a5e43</citedby><cites>FETCH-LOGICAL-c692t-77cd5529c13ac6c7dd3673ac57bb1df6541e7d32e88dfe0b36a89fa55052a5e43</cites><orcidid>0000-0003-2018-9595</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9191727/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9191727/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35696403$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Mohammadzadeh, Ardashir</contributor><creatorcontrib>Zhao, Zhenhua</creatorcontrib><creatorcontrib>Feng, Guohua</creatorcontrib><creatorcontrib>Zhang, Jing</creatorcontrib><title>The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The accurate prediction of reference crop evapotranspiration is of great significance to climate research and regional agricultural water management. In order to realize the high-precision prediction of ETO in the absence of meteorological data, this study use XGBoost to select key influencing factors and BP algorithm to construct ETO prediction model of 12 meteorological stations in South West China in this study. ACO, CSO and CS algorithms are used to optimize the model and improve the adaptability of the model. The results show that Tmax, n and Ra can be used as the input combination of ETO model construction, and Tmax is the primary factor affecting ETO. ETO model constructed by BP algorithm has good goodness of fit with the ETO calculated by FAO-56 PM and ACO, CSO and CS have significant optimization effect on BP algorithm, among which CSO algorithm has the best optimization ability on BP, with RMSE, R2, MAE, NSE, GPI ranging 0.200-0.377, 0.932-0.984, 0.140-0.261, 0.920-0.984, 1.472-2.000, GPI ranking is 1-23. Therefore, the input combination (Tmax, n and Ra) and CSO-BP model are recommended as a simplified model for ETO prediction in Southwest China.</description><subject>Accuracy</subject><subject>Adaptability</subject><subject>Agricultural management</subject><subject>Agricultural research</subject><subject>Agriculture</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Ant colony optimization</subject><subject>Biology and Life Sciences</subject><subject>China</subject><subject>Climate</subject><subject>Climate change</subject><subject>Computer and Information Sciences</subject><subject>Crop evapotranspiration</subject><subject>Earth Sciences</subject><subject>Evapotranspiration</subject><subject>Goodness of fit</subject><subject>Humidity</subject><subject>Hydrologic cycle</subject><subject>Machine learning</subject><subject>Meteorological data</subject><subject>Meteorology</subject><subject>Modelling</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Physical Sciences</subject><subject>Precipitation</subject><subject>Prediction models</subject><subject>Propagation</subject><subject>Radiation</subject><subject>Regional climates</subject><subject>Research and Analysis Methods</subject><subject>Social Sciences</subject><subject>Support vector machines</subject><subject>Water management</subject><subject>Weather stations</subject><subject>Wind</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk12L1DAUhoso7rr6D0QDgujFjPlok_ZGWAc_BhZW3NXbkCan04ydppu0q_vvTZ3uMpW9kFIaTp_3bfPmnCR5TvCSMEHebd3gW9UsO9fCElNeiJQ_SI5JweiCU8weHqyPkichbDHOWM754-SIZbzgKWbHyc_LGlCwu66xlQWD6pvSW4N2zkCDShViybXow1fUO9R5MFb3qI8SDxV4aDUg7V2H4Fp1rveqDZ31qrdRY1t04Ya-_gWhR6vatupp8qhSTYBn0_Mk-f7p4-Xqy-Ls_PN6dXq20Lyg_UIIbbKMFpowpbkWxjAu4jITZUlMxbOUgDCMQp6bCnDJuMqLSmUZzqjKIGUnycu9b9e4IKecgqQ85_EmOY3Eek8Yp7ay83an_I10ysq_Bec3Uvne6gYk4SmkVFBDcpOCSEtTciIKSnFqcMFZ9Ho_fW0od2A0tDGHZmY6f9PaWm7ctSxIQQQV0eDNZODd1RDTkjsbNDSNasEN43-LrMhphCP66h_0_t1N1EbFDdi2Gk9Gj6byVGCBMc9FFqnlPVS8DOysjk1V2VifCd7OBJHp4Xe_UUMIcn3x7f_Z8x9z9vUBW4Nq-jq4Zhi7KMzBdA_GlgshduBdyATLcSZu05DjTMhpJqLsxeEB3Yluh4D9AXO7BtA</recordid><startdate>20220613</startdate><enddate>20220613</enddate><creator>Zhao, Zhenhua</creator><creator>Feng, Guohua</creator><creator>Zhang, Jing</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-2018-9595</orcidid></search><sort><creationdate>20220613</creationdate><title>The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China</title><author>Zhao, Zhenhua ; Feng, Guohua ; Zhang, Jing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-77cd5529c13ac6c7dd3673ac57bb1df6541e7d32e88dfe0b36a89fa55052a5e43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Adaptability</topic><topic>Agricultural management</topic><topic>Agricultural research</topic><topic>Agriculture</topic><topic>Algorithms</topic><topic>Analysis</topic><topic>Ant colony optimization</topic><topic>Biology and Life Sciences</topic><topic>China</topic><topic>Climate</topic><topic>Climate change</topic><topic>Computer and Information Sciences</topic><topic>Crop evapotranspiration</topic><topic>Earth Sciences</topic><topic>Evapotranspiration</topic><topic>Goodness of fit</topic><topic>Humidity</topic><topic>Hydrologic cycle</topic><topic>Machine learning</topic><topic>Meteorological data</topic><topic>Meteorology</topic><topic>Modelling</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Physical Sciences</topic><topic>Precipitation</topic><topic>Prediction models</topic><topic>Propagation</topic><topic>Radiation</topic><topic>Regional climates</topic><topic>Research and Analysis Methods</topic><topic>Social Sciences</topic><topic>Support vector machines</topic><topic>Water management</topic><topic>Weather stations</topic><topic>Wind</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Zhenhua</creatorcontrib><creatorcontrib>Feng, Guohua</creatorcontrib><creatorcontrib>Zhang, Jing</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</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>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</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>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Zhenhua</au><au>Feng, Guohua</au><au>Zhang, Jing</au><au>Mohammadzadeh, Ardashir</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-06-13</date><risdate>2022</risdate><volume>17</volume><issue>6</issue><spage>e0269746</spage><epage>e0269746</epage><pages>e0269746-e0269746</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The accurate prediction of reference crop evapotranspiration is of great significance to climate research and regional agricultural water management. In order to realize the high-precision prediction of ETO in the absence of meteorological data, this study use XGBoost to select key influencing factors and BP algorithm to construct ETO prediction model of 12 meteorological stations in South West China in this study. ACO, CSO and CS algorithms are used to optimize the model and improve the adaptability of the model. The results show that Tmax, n and Ra can be used as the input combination of ETO model construction, and Tmax is the primary factor affecting ETO. ETO model constructed by BP algorithm has good goodness of fit with the ETO calculated by FAO-56 PM and ACO, CSO and CS have significant optimization effect on BP algorithm, among which CSO algorithm has the best optimization ability on BP, with RMSE, R2, MAE, NSE, GPI ranging 0.200-0.377, 0.932-0.984, 0.140-0.261, 0.920-0.984, 1.472-2.000, GPI ranking is 1-23. Therefore, the input combination (Tmax, n and Ra) and CSO-BP model are recommended as a simplified model for ETO prediction in Southwest China.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35696403</pmid><doi>10.1371/journal.pone.0269746</doi><tpages>e0269746</tpages><orcidid>https://orcid.org/0000-0003-2018-9595</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2022-06, Vol.17 (6), p.e0269746-e0269746 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2686268182 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accuracy Adaptability Agricultural management Agricultural research Agriculture Algorithms Analysis Ant colony optimization Biology and Life Sciences China Climate Climate change Computer and Information Sciences Crop evapotranspiration Earth Sciences Evapotranspiration Goodness of fit Humidity Hydrologic cycle Machine learning Meteorological data Meteorology Modelling Neural networks Neurons Optimization Optimization algorithms Physical Sciences Precipitation Prediction models Propagation Radiation Regional climates Research and Analysis Methods Social Sciences Support vector machines Water management Weather stations Wind |
| title | The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T09%3A38%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20simplified%20hybrid%20model%20based%20on%20BP%20to%20predict%20the%20reference%20crop%20evapotranspiration%20in%20Southwest%20China&rft.jtitle=PloS%20one&rft.au=Zhao,%20Zhenhua&rft.date=2022-06-13&rft.volume=17&rft.issue=6&rft.spage=e0269746&rft.epage=e0269746&rft.pages=e0269746-e0269746&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0269746&rft_dat=%3Cgale_plos_%3EA707006875%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2686268182&rft_id=info:pmid/35696403&rft_galeid=A707006875&rft_doaj_id=oai_doaj_org_article_164e4272d18d4e74bdb61792204d0963&rfr_iscdi=true |