Machine Learning based Regression Model for Prediction of Soil Surface Humidity over Moderately Vegetated Fields

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dc.contributor.authorAcar, Emrullah
dc.contributor.authorOzerdem, Mehmet Sirac
dc.contributor.authorUstundag, Burak Berk
dc.date.accessioned2024-04-24T17:11:22Z
dc.date.available2024-04-24T17:11:22Z
dc.date.issued2019
dc.departmentDicle Üniversitesien_US
dc.description8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) -- JUL 16-19, 2019 -- Istanbul, TURKEYen_US
dc.description.abstractThe soil surface humidity parameter over vegetated fields is of great importance for controlling water consumption; prevention of salinity caused by over-irrigation; efficient use of irrigation system and improving the yield and quality of the cultivated crop. However, determination of the soil surface humidity is very difficult on vegetated fields. In order to overcome this problem, polarimetric decomposition models and machine learning based regression model were implemented. The main purpose of this study is to predict soil surface humidity on moderately vegetated fields. Thus, the study is conducted in agricultural fields of Dicle University and it consists of several stages. In the first stage, a Radarsat-2 data was obtained in 3 March 2016 and the local humidity samples were measured simultaneously with the Radarsat-2 acquisition. In the second stage, 10 polarimetric features were obtained from each cell (2x2 pixels) of ground sample by utilizing standard intensity-phase technique as well as Freeman-Durden and H/A/alpha polarimetric decomposition models. This step is repeated for all ground samples and as a result, a dataset with 156x10 lengths is formed. In the next stage, Extreme Learning Machine based Regression (ELM-R) model was used for predicting the soil surface humidity with the aid of polarimetric SAR features. For the validation of the proposed system, leave-one-out cross-validation method was applied and finally, 2.19% Root Mean Square Error (RMSE) were computed.en_US
dc.description.sponsorshipGeorge Mason Univ, Ctr Spatial Informat Sci & Syst,Istanbul Techn Univ,TARBIL Agr Informat Appl Res Ctr,CSISS Fdn Inc,USDA NIFA,Inst Elect & Elect Engineers,IEEE Geoscience & Remote Sensing Soc,Open Geospatial Consortiumen_US
dc.identifier.doi10.1109/agro-geoinformatics.2019.8820461
dc.identifier.isbn978-1-7281-2116-1
dc.identifier.issn2334-3168
dc.identifier.urihttps://doi.org/10.1109/agro-geoinformatics.2019.8820461
dc.identifier.urihttps://hdl.handle.net/11468/17446
dc.identifier.wosWOS:000562356600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectExtreme Learning Machine Based Regression (Elm-R)en_US
dc.subjectPredictionen_US
dc.subjectSoil Surface Humidityen_US
dc.subjectSaren_US
dc.titleMachine Learning based Regression Model for Prediction of Soil Surface Humidity over Moderately Vegetated Fieldsen_US
dc.typeConference Objecten_US

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