Publication:
An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration

Simple item page
dc.citedby38
dc.contributor.authorEhteram M.en_US
dc.contributor.authorSingh V.P.en_US
dc.contributor.authorFerdowsi A.en_US
dc.contributor.authorMousavi S.F.en_US
dc.contributor.authorFarzin S.en_US
dc.contributor.authorKarami H.en_US
dc.contributor.authorMohd N.S.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorLai S.H.en_US
dc.contributor.authorKisi O.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57211219633en_US
dc.contributor.authorid57207964868en_US
dc.contributor.authorid7003344568en_US
dc.contributor.authorid55315758000en_US
dc.contributor.authorid36863982200en_US
dc.contributor.authorid57192892703en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid36102664300en_US
dc.contributor.authorid6507051085en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T07:25:48Z
dc.date.available2023-05-29T07:25:48Z
dc.date.issued2019
dc.descriptionarticle; evapotranspiration; fuzzy system; genetic model; humidity; India; simulation; support vector machine; wind speed; environmental monitoring; fuzzy logic; irrigation (agriculture); procedures; river; support vector machine; temperature; wind; Agricultural Irrigation; Environmental Monitoring; Fuzzy Logic; Rivers; Support Vector Machine; Temperature; Winden_US
dc.description.abstractReference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5-15% and 5-17% compared with the GP model, 12-21% and 10-22% compared with the M5T model, and 7-15% and 5-18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models. � 2019 Ehteram et al.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNoe0217499
dc.identifier.doi10.1371/journal.pone.0217499
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85066480927
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85066480927&doi=10.1371%2fjournal.pone.0217499&partnerID=40&md5=bd9885bb83bc7c1294d98b023cf42e54
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24681
dc.identifier.volume14
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitlePLoS ONE
dc.titleAn improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspirationen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS