An intelligent multi-output regression model for soil moisture prediction

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dc.authorscopusid57264876100
dc.authorscopusid6508164583
dc.authorscopusid57211394455
dc.contributor.authorKüçük, Cansel
dc.contributor.authorBirant, Derya
dc.contributor.authorYıldırım Taşer, Pelin
dc.date.accessioned2022-02-15T16:58:05Z
dc.date.available2022-02-15T16:58:05Z
dc.date.issued2022
dc.departmentBakırçay Üniversitesien_US
dc.descriptionInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021 -- 24 August 2021 through 26 August 2021 -- -- 264409en_US
dc.description.abstractSoil moisture prediction plays a vital role in developing plants, soil properties, and sustenance of agricultural systems. Considering this motivation, in this study, an intelligent Multi-output regression method was implemented on daily values of meteorological and soil data obtained from Kemalpaşa-Örnekköy station in Izmir, Turkey, at three soil depths (15, 30, and 45 cm) between the years 2017 and 2019. In this study, nine different machine learning algorithms (Linear Regression (LR), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso), Random Forest (RF), Extra Tree Regression (ETR), Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Histogram-Based Gradient Boosting (HGB)) were compared each other in terms of MAE, RMSE, and R2 metrics. The experiments indicate that the implemented Multi-output regression models show good soil moisture prediction performance. Also, the ETR algorithm provided the best prediction performance with an 0.81 R2 value among the other models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.description.sponsorshipThe authors are deeply grateful to Selim Alpaslan in the Izmir Metropolitan Municipality for providing the experimental dataset used in the study.en_US
dc.identifier.doi10.1007/978-3-030-85577-2_56
dc.identifier.endpage481en_US
dc.identifier.isbn9783030855765
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85115274014en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage474en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-85577-2_56
dc.identifier.urihttps://hdl.handle.net/20.500.14034/343
dc.identifier.volume308en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.journalLecture Notes in Networks and Systemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectIntelligent multi-output regressionen_US
dc.subjectMachine Learningen_US
dc.subjectRegressionen_US
dc.subjectSoil Moisture Predictionen_US
dc.titleAn intelligent multi-output regression model for soil moisture predictionen_US
dc.typeConference Objecten_US

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