SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporation.
Identifieur interne : 000616 ( Main/Exploration ); précédent : 000615; suivant : 000617SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporation.
Auteurs : Behrooz Keshtegar [Viêt Nam] ; Salim Heddam [Algérie] ; Abderrazek Sebbar [Algérie] ; Shun-Peng Zhu [République populaire de Chine] ; Nguyen-Thoi Trung [Viêt Nam]Source :
- Environmental science and pollution research international [ 1614-7499 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
- méthodes : Surveillance de l'environnement.
- Algérie, Analyse de régression, Heuristique, Vent.
- Wicri :
- geographic : Algérie.
English descriptors
- KwdEn :
- MESH :
- geographic : Algeria.
- methods : Environmental Monitoring.
- Heuristics, Neural Networks, Computer, Regression Analysis, Wind.
Abstract
In the present study, a hybrid intelligent model called SVR_RSM, which was extracted using response surface method (RSM) combined by the support vector regression (SVR) approaches was applied for predicting monthly pan evaporation (Epan). This method is established based on two basic calibrating process using RSM and SVR. In the first process, an input data group with two different input variables are used to calibrate the RSM; hence, the calibrating data by RSM in the first process are applied as input database for calibrating the SVR in the second process. Results obtained using the proposed SVR_RSM was compared with those obtained using the RSM, SVR, and the well-known multilayer perceptron neural network (MLPNN) models. Climatic variables including maximum and minimum temperatures (Tmax, Tmin), wind speed (U2), and relative humidity (H%), and the periodicity represented by the month number (α) were selected for predicting the monthly Epan measured with the standard class A evaporation pan. Data was collected at six climatic stations located at the northern East of Algeria. The performances of the proposed models were compared using the RMSE, MAE, modified index of agreement (d), coefficient of correlation (R), and modified Nash and Sutcliffe efficiency (NSE). Using various input combination, the results show that the hybrid SVR_RSM model performed better than all the proposed models. Overall, better accuracy was observed when the model contained the periodicity (α), and it was demonstrated that the best accuracy was obtained using only Tmax and Tmin, coupled with the periodicity.
DOI: 10.1007/s11356-019-06596-8
PubMed: 31705408
Affiliations:
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beh.keshtegar@tdtu.edu.vn.</nlm:affiliation><country xml:lang="fr">Viêt Nam</country><wicri:regionArea>Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City</wicri:regionArea><wicri:noRegion>Ho Chi Minh City</wicri:noRegion></affiliation></author><author><name sortKey="Heddam, Salim" sort="Heddam, Salim" uniqKey="Heddam S" first="Salim" last="Heddam">Salim Heddam</name><affiliation wicri:level="1"><nlm:affiliation>Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, 26, Skikda, BP, Algeria. heddamsalim@yahoo.fr.</nlm:affiliation><country xml:lang="fr">Algérie</country><wicri:regionArea>Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, 26, Skikda, BP</wicri:regionArea><wicri:noRegion>BP</wicri:noRegion></affiliation></author><author><name sortKey="Sebbar, Abderrazek" sort="Sebbar, Abderrazek" uniqKey="Sebbar A" first="Abderrazek" last="Sebbar">Abderrazek Sebbar</name><affiliation wicri:level="1"><nlm:affiliation>Soil and Hydraulics Research Laboratory, Faculty of Engineering Sciences, Hydraulics Department, University Badji-Mokhtar, Annaba, Algeria.</nlm:affiliation><country xml:lang="fr">Algérie</country><wicri:regionArea>Soil and Hydraulics Research Laboratory, Faculty of Engineering Sciences, Hydraulics Department, University Badji-Mokhtar, Annaba</wicri:regionArea><wicri:noRegion>Annaba</wicri:noRegion></affiliation></author><author><name sortKey="Zhu, Shun Peng" sort="Zhu, Shun Peng" uniqKey="Zhu S" first="Shun-Peng" last="Zhu">Shun-Peng Zhu</name><affiliation wicri:level="1"><nlm:affiliation>School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731</wicri:regionArea><wicri:noRegion>611731</wicri:noRegion></affiliation></author><author><name sortKey="Trung, Nguyen Thoi" sort="Trung, Nguyen Thoi" uniqKey="Trung N" first="Nguyen-Thoi" last="Trung">Nguyen-Thoi Trung</name><affiliation wicri:level="1"><nlm:affiliation>Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam.</nlm:affiliation><country xml:lang="fr">Viêt Nam</country><wicri:regionArea>Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City</wicri:regionArea><wicri:noRegion>Ho Chi Minh City</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:affiliation>Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.</nlm:affiliation><country xml:lang="fr">Viêt Nam</country><wicri:regionArea>Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City</wicri:regionArea><wicri:noRegion>Ho Chi Minh City</wicri:noRegion></affiliation></author></analytic><series><title level="j">Environmental science and pollution research international</title><idno type="eISSN">1614-7499</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algeria (MeSH)</term><term>Environmental Monitoring (methods)</term><term>Heuristics (MeSH)</term><term>Neural Networks, Computer (MeSH)</term><term>Regression Analysis (MeSH)</term><term>Wind (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algérie (MeSH)</term><term>Analyse de régression (MeSH)</term><term>Heuristique (MeSH)</term><term>Surveillance de l'environnement (méthodes)</term><term>Vent (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>Algeria</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Environmental Monitoring</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Surveillance de l'environnement</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Heuristics</term><term>Neural Networks, Computer</term><term>Regression Analysis</term><term>Wind</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algérie</term><term>Analyse de régression</term><term>Heuristique</term><term>Vent</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Algérie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In the present study, a hybrid intelligent model called SVR_RSM, which was extracted using response surface method (RSM) combined by the support vector regression (SVR) approaches was applied for predicting monthly pan evaporation (E<sub>pan</sub>). This method is established based on two basic calibrating process using RSM and SVR. In the first process, an input data group with two different input variables are used to calibrate the RSM; hence, the calibrating data by RSM in the first process are applied as input database for calibrating the SVR in the second process. Results obtained using the proposed SVR_RSM was compared with those obtained using the RSM, SVR, and the well-known multilayer perceptron neural network (MLPNN) models. Climatic variables including maximum and minimum temperatures (T<sub>max</sub>, T<sub>min</sub>), wind speed (U<sub>2</sub>), and relative humidity (H%), and the periodicity represented by the month number (α) were selected for predicting the monthly E<sub>pan</sub> measured with the standard class A evaporation pan. Data was collected at six climatic stations located at the northern East of Algeria. The performances of the proposed models were compared using the RMSE, MAE, modified index of agreement (d), coefficient of correlation (R), and modified Nash and Sutcliffe efficiency (NSE). Using various input combination, the results show that the hybrid SVR_RSM model performed better than all the proposed models. Overall, better accuracy was observed when the model contained the periodicity (α), and it was demonstrated that the best accuracy was obtained using only T<sub>max</sub> and T<sub>min</sub>, coupled with the periodicity.</div></front></TEI><affiliations><list><country><li>Algérie</li><li>République populaire de Chine</li><li>Viêt Nam</li></country></list><tree><country name="Viêt Nam"><noRegion><name sortKey="Keshtegar, Behrooz" sort="Keshtegar, Behrooz" uniqKey="Keshtegar B" first="Behrooz" last="Keshtegar">Behrooz Keshtegar</name></noRegion><name sortKey="Keshtegar, Behrooz" sort="Keshtegar, Behrooz" uniqKey="Keshtegar B" first="Behrooz" last="Keshtegar">Behrooz Keshtegar</name><name sortKey="Trung, Nguyen Thoi" sort="Trung, Nguyen Thoi" uniqKey="Trung N" first="Nguyen-Thoi" last="Trung">Nguyen-Thoi Trung</name><name sortKey="Trung, Nguyen Thoi" sort="Trung, Nguyen Thoi" uniqKey="Trung N" first="Nguyen-Thoi" last="Trung">Nguyen-Thoi Trung</name></country><country name="Algérie"><noRegion><name sortKey="Heddam, Salim" sort="Heddam, Salim" uniqKey="Heddam S" first="Salim" last="Heddam">Salim Heddam</name></noRegion><name sortKey="Sebbar, Abderrazek" sort="Sebbar, Abderrazek" uniqKey="Sebbar A" first="Abderrazek" last="Sebbar">Abderrazek Sebbar</name></country><country name="République populaire de Chine"><noRegion><name sortKey="Zhu, Shun Peng" sort="Zhu, Shun Peng" uniqKey="Zhu S" first="Shun-Peng" last="Zhu">Shun-Peng Zhu</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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