[Modeling water consumption of Populus bolleana by artificial neural network based on fuzzy rules].
Identifieur interne : 000009 ( Main/Exploration ); précédent : 000008; suivant : 000010[Modeling water consumption of Populus bolleana by artificial neural network based on fuzzy rules].
Auteurs : Yong-Gui Han [République populaire de Chine] ; Yang Gao [République populaire de Chine] ; Lei Han [République populaire de Chine] ; Xiao-Yu Huang [République populaire de Chine]Source :
- Ying yong sheng tai xue bao = The journal of applied ecology [ 1001-9332 ] ; 2020.
Descripteurs français
- KwdFr :
- MESH :
- Wicri :
- geographic : République populaire de Chine.
English descriptors
- KwdEn :
- MESH :
- chemical : Water.
- geographic : China.
- Drinking, Neural Networks, Computer, Plant Transpiration, Populus, Trees.
Abstract
To explore the water consumption characteristics of trees, the thermal dissipation probe technology was used to monitor sap flow of Populus bolleana in east sandy land of Yellow River, from July to November in 2017. Microclimate variables were monitored. We analyzed the diurnal and seasonal variations of water consumption, and proposed the models for water consumption with back propagation neural network (BPNN) and Elman neural network (ENN) based on fuzzy rules. Results showed that the average sap flow rate of P. bolleana was 4.98 g·cm-2·h-1 in growing season (July to October), with solar radiation (Rs), temperature (T), vapor pressure deficit (VPD) and relative humidity (RH) as the main factors affecting sap flow. Due to the influence of meteorological factors, water consumption was characterized by obvious seasonal variation, with that in summer (July-August) being 1.4 times of that in autumn (September-October). BPNN and ENN models based on fuzzy rules were used to simulate water consumption of P. euphratica. The optimal parameter calibration of two models explained more than 80% of the total variation, which indicated that these two models could more accurately simulate water consumption. Compared with the BP neural network model, the simulated results of ENN model showed that the relative error was reduced by 27.0%, RMSE was reduced by 24.3%, Nash-Sutclife efficiency coefficient increased by 67.9%, R2 was higher than 0.80. The ENN model performed better than BPNN model with a higher efficiency and goodness of fitness. ENN model effectively improved the simulating accuracy of water consumption. Therefore, it could be used as an optimal model to estimate water consumption of P. bolleana in east sandy land of Yellow River.
DOI: 10.13287/j.1001-9332.202005.005
PubMed: 32530230
Affiliations:
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Resources and Environmental Governance in Arid Regions, Ningxia University, Yinchuan 750021</wicri:regionArea><wicri:noRegion>Yinchuan 750021</wicri:noRegion></affiliation></author><author><name sortKey="Gao, Yang" sort="Gao, Yang" uniqKey="Gao Y" first="Yang" last="Gao">Yang Gao</name><affiliation wicri:level="1"><nlm:affiliation>College of Recourses and Environmental Science, China-Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Regions, Ningxia University, Yinchuan 750021, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Recourses and Environmental Science, China-Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Regions, Ningxia University, Yinchuan 750021</wicri:regionArea><wicri:noRegion>Yinchuan 750021</wicri:noRegion></affiliation></author><author><name sortKey="Han, Lei" sort="Han, Lei" uniqKey="Han L" first="Lei" last="Han">Lei Han</name><affiliation wicri:level="1"><nlm:affiliation>College of Recourses and Environmental Science, China-Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Regions, Ningxia University, Yinchuan 750021, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Recourses and Environmental Science, China-Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Regions, Ningxia University, Yinchuan 750021</wicri:regionArea><wicri:noRegion>Yinchuan 750021</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:affiliation>Institute of Environmental Engineering, Ningxia University, Yinchuan 750021, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Environmental Engineering, Ningxia University, Yinchuan 750021</wicri:regionArea><wicri:noRegion>Yinchuan 750021</wicri:noRegion></affiliation></author><author><name sortKey="Huang, Xiao Yu" sort="Huang, Xiao Yu" uniqKey="Huang X" first="Xiao-Yu" last="Huang">Xiao-Yu Huang</name><affiliation wicri:level="1"><nlm:affiliation>College of Recourses and Environmental Science, China-Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Regions, Ningxia University, Yinchuan 750021, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Recourses and Environmental Science, China-Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Regions, Ningxia University, Yinchuan 750021</wicri:regionArea><wicri:noRegion>Yinchuan 750021</wicri:noRegion></affiliation></author></analytic><series><title level="j">Ying yong sheng tai xue bao = The journal of applied ecology</title><idno type="ISSN">1001-9332</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>China (MeSH)</term><term>Drinking (MeSH)</term><term>Neural Networks, Computer (MeSH)</term><term>Plant Transpiration (MeSH)</term><term>Populus (MeSH)</term><term>Trees (MeSH)</term><term>Water (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Arbres (MeSH)</term><term>Chine (MeSH)</term><term>Consommation de boisson (MeSH)</term><term>Eau (MeSH)</term><term>Populus (MeSH)</term><term>Transpiration des plantes (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>Water</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>China</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Drinking</term><term>Neural Networks, Computer</term><term>Plant Transpiration</term><term>Populus</term><term>Trees</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Arbres</term><term>Chine</term><term>Consommation de boisson</term><term>Eau</term><term>Populus</term><term>Transpiration des plantes</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>République populaire de Chine</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">To explore the water consumption characteristics of trees, the thermal dissipation probe technology was used to monitor sap flow of Populus bolleana in east sandy land of Yellow River, from July to November in 2017. Microclimate variables were monitored. We analyzed the diurnal and seasonal variations of water consumption, and proposed the models for water consumption with back propagation neural network (BPNN) and Elman neural network (ENN) based on fuzzy rules. Results showed that the average sap flow rate of P. bolleana was 4.98 g·cm<sup>-2</sup>·h<sup>-1</sup> in growing season (July to October), with solar radiation (R<sub>s</sub>), temperature (T), vapor pressure deficit (VPD) and relative humidity (RH) as the main factors affecting sap flow. Due to the influence of meteorological factors, water consumption was characterized by obvious seasonal variation, with that in summer (July-August) being 1.4 times of that in autumn (September-October). BPNN and ENN models based on fuzzy rules were used to simulate water consumption of P. euphratica. The optimal parameter calibration of two models explained more than 80% of the total variation, which indicated that these two models could more accurately simulate water consumption. Compared with the BP neural network model, the simulated results of ENN model showed that the relative error was reduced by 27.0%, RMSE was reduced by 24.3%, Nash-Sutclife efficiency coefficient increased by 67.9%, R<sup>2</sup> was higher than 0.80. The ENN model performed better than BPNN model with a higher efficiency and goodness of fitness. ENN model effectively improved the simulating accuracy of water consumption. Therefore, it could be used as an optimal model to estimate water consumption of P. bolleana in east sandy land of Yellow River.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Automated" Owner="NLM"><PMID Version="1">32530230</PMID><DateCompleted><Year>2020</Year><Month>06</Month><Day>15</Day></DateCompleted><DateRevised><Year>2020</Year><Month>06</Month><Day>15</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">1001-9332</ISSN><JournalIssue CitedMedium="Print"><Volume>31</Volume><Issue>5</Issue><PubDate><Year>2020</Year><Month>May</Month></PubDate></JournalIssue><Title>Ying yong sheng tai xue bao = The journal of applied ecology</Title><ISOAbbreviation>Ying Yong Sheng Tai Xue Bao</ISOAbbreviation></Journal><ArticleTitle>[Modeling water consumption of Populus bolleana by artificial neural network based on fuzzy rules].</ArticleTitle><Pagination><MedlinePgn>1525-1534</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.13287/j.1001-9332.202005.005</ELocationID><Abstract><AbstractText>To explore the water consumption characteristics of trees, the thermal dissipation probe technology was used to monitor sap flow of Populus bolleana in east sandy land of Yellow River, from July to November in 2017. Microclimate variables were monitored. We analyzed the diurnal and seasonal variations of water consumption, and proposed the models for water consumption with back propagation neural network (BPNN) and Elman neural network (ENN) based on fuzzy rules. Results showed that the average sap flow rate of P. bolleana was 4.98 g·cm<sup>-2</sup>·h<sup>-1</sup> in growing season (July to October), with solar radiation (R<sub>s</sub>), temperature (T), vapor pressure deficit (VPD) and relative humidity (RH) as the main factors affecting sap flow. Due to the influence of meteorological factors, water consumption was characterized by obvious seasonal variation, with that in summer (July-August) being 1.4 times of that in autumn (September-October). BPNN and ENN models based on fuzzy rules were used to simulate water consumption of P. euphratica. The optimal parameter calibration of two models explained more than 80% of the total variation, which indicated that these two models could more accurately simulate water consumption. Compared with the BP neural network model, the simulated results of ENN model showed that the relative error was reduced by 27.0%, RMSE was reduced by 24.3%, Nash-Sutclife efficiency coefficient increased by 67.9%, R<sup>2</sup> was higher than 0.80. The ENN model performed better than BPNN model with a higher efficiency and goodness of fitness. ENN model effectively improved the simulating accuracy of water consumption. Therefore, it could be used as an optimal model to estimate water consumption of P. bolleana in east sandy land of Yellow River.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Han</LastName><ForeName>Yong-Gui</ForeName><Initials>YG</Initials><AffiliationInfo><Affiliation>College of Recourses and Environmental Science, China-Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Regions, Ningxia University, Yinchuan 750021, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gao</LastName><ForeName>Yang</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>College of Recourses and Environmental Science, China-Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Regions, Ningxia University, Yinchuan 750021, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Han</LastName><ForeName>Lei</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>College of Recourses and Environmental Science, China-Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Regions, Ningxia University, Yinchuan 750021, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Institute of Environmental Engineering, Ningxia University, Yinchuan 750021, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Huang</LastName><ForeName>Xiao-Yu</ForeName><Initials>XY</Initials><AffiliationInfo><Affiliation>College of Recourses and Environmental Science, China-Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Regions, Ningxia University, Yinchuan 750021, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>chi</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><VernacularTitle>基于模糊规则的人工神经网络模拟新疆杨蒸腾耗水.</VernacularTitle></Article><MedlineJournalInfo><Country>China</Country><MedlineTA>Ying Yong Sheng Tai Xue Bao</MedlineTA><NlmUniqueID>9425159</NlmUniqueID><ISSNLinking>1001-9332</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>059QF0KO0R</RegistryNumber><NameOfSubstance UI="D014867">Water</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D002681" MajorTopicYN="N" Type="Geographic">China</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004326" MajorTopicYN="N">Drinking</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016571" MajorTopicYN="N">Neural Networks, Computer</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018526" MajorTopicYN="N">Plant Transpiration</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="Y">Populus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014197" MajorTopicYN="N">Trees</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014867" MajorTopicYN="N">Water</DescriptorName></MeshHeading></MeshHeadingList><OtherAbstract Type="Publisher" Language="chi"><AbstractText>于2017年7—11月,应用热扩散探针(TDP)技术,结合同步测定的气象因子,对宁夏河东沙区新疆杨的耗水日变化特征及季节变化规律进行分析,提出了一种基于模糊规则的BP神经网络和Elman神经网络耗水模型,探究新疆杨蒸腾耗水规律并对其耗水量进行模拟。结果表明: 生长季内(7—10月)新疆杨平均液流密度为4.98 g·cm<sup>-2</sup>·h<sup>-1</sup>,影响蒸腾耗水的主要因素依次为太阳辐射、大气温度、饱和水汽压亏缺和相对湿度;受气象因子影响,新疆杨耗水具有明显的季节性变化规律,夏季(7—8月)单株耗水量为秋季(9—10月)的1.4倍;采用基于模糊规则的BP神经网络和Elman神经网络模型对新疆杨耗水进行模拟可以解释80%以上的变量,能够较准确地模拟新疆杨耗水情况,相对于BP神经网络,采用Elman神经网络对新疆杨耗水进行模拟,相对误差减少27.0%,均方根误差减少24.3%,纳什效率系数提高67.9%,决定系数达0.80以上。Elman神经网络的模拟效果优于BP神经网络,模型效率和拟合度更高,有效地提高了林木蒸腾耗水模拟精度,可作为河东沙区新疆杨林分蒸腾耗水估算的首选模型。.</AbstractText></OtherAbstract><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Populus bolleana</Keyword><Keyword MajorTopicYN="N">neural network</Keyword><Keyword MajorTopicYN="N">sap flow</Keyword><Keyword MajorTopicYN="N">water consumption</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>6</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>6</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>6</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32530230</ArticleId><ArticleId IdType="doi">10.13287/j.1001-9332.202005.005</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>République populaire de Chine</li></country></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Han, Yong Gui" sort="Han, Yong Gui" uniqKey="Han Y" first="Yong-Gui" last="Han">Yong-Gui Han</name></noRegion><name sortKey="Gao, Yang" sort="Gao, Yang" uniqKey="Gao Y" first="Yang" last="Gao">Yang Gao</name><name sortKey="Han, Lei" sort="Han, Lei" uniqKey="Han L" first="Lei" last="Han">Lei Han</name><name sortKey="Han, Lei" sort="Han, Lei" uniqKey="Han L" first="Lei" last="Han">Lei Han</name><name sortKey="Huang, Xiao Yu" sort="Huang, Xiao Yu" uniqKey="Huang X" first="Xiao-Yu" last="Huang">Xiao-Yu Huang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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