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dc.contributor.authorBilgilioğlu, Burhan Baha
dc.contributor.authorDervisoglu, Adalet
dc.contributor.authorYagmur, Nur
dc.date.accessioned2023-01-30T07:21:50Z
dc.date.available2023-01-30T07:21:50Z
dc.date.issued2021en_US
dc.identifier.urihttps://www.deswater.com/DWT_abstracts/vol_243/243_2021_231.pdf
dc.identifier.urihttps://hdl.handle.net/20.500.12440/5596
dc.description.abstractRemote sensing (RS) effectively identifies, analyzes, and monitors wetlands. In addition to these two-dimensional studies, RS is used with several techniques in determining shallow water depths. The primary purpose of this study is to obtain shallow wetland bathymetry utilizing spectral reflections obtained at different water depths by field study and satellite images. Machine learning (ML) algorithms, which are widely used in remote sensing, are used in this study. Four algorithms were selected as random forest (RF), support vector machine (SVM), Neural Networks (NN), and Maximum Likelihood Classification (MLC). Since machine learning algorithms use training samples/datasets, the classification accuracy is directly related to selecting these data. The effect of pixel counts on classification was investigated by using two different training data set also. Duden (Kulu) Lake, which is a shallow wetland, was chosen as the study area. The Iterative Self-Organizing Data Analysis Technique (ISODATA) classification algorithm divided into as many clusters as possible was applied on Sentinel-2 multispectral images. All classes were rede-fined using measured spectral signatures and were created a bathymetric map. This map was used as reference data in creating training sets and the accuracy assessment of ML algorithms. When the water surface areas obtained from algorithms were compared with the bathymetric map and Normalized Difference Water Index, the best result was obtained with RF. According to the accuracy assessment results, it was seen that the number of training data affects the accuracy, and the best results were obtained with SVM and RF algorithms with training data containing more pixels (overall accuracy 93.87% and 92.64, kappa 0.89 and 0.87, respectively). © 2021 Desalination Publications. All rights reserved.en_US
dc.language.isoengen_US
dc.publisherDesalination Publicationsen_US
dc.relation.ispartofDesalination and Water Treatmenten_US
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_US
dc.subjectMachine learningen_US
dc.subjectMaximum likelihood classificationen_US
dc.subjectNeural networksen_US
dc.subjectRandom foresten_US
dc.subjectRemote sensingen_US
dc.subjectSatellite-derived bathymetryen_US
dc.subjectSupport vector machineen_US
dc.subjectWetlanden_US
dc.titleSatellite-derived shallow wetland bathymetry using different classification algorithms and datasetsen_US
dc.typearticleen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıen_US
dc.description.wospublicationidWOS:000747966300021en_US
dc.departmentMeslek Yüksekokulları, Gümüşhane Meslek Yüksekokulu, Mimarlık ve Şehir Planlama Bölümüen_US
dc.authorid0000-0001-6950-4336en_US
dc.identifier.volume243en_US
dc.identifier.startpage231en_US
dc.contributor.institutionauthorBilgilioğlu, Burhan Baha
dc.identifier.doi10.5004/dwt.2021.27857en_US
dc.identifier.endpage241en_US
dc.authorwosidHFI-6490-2022en_US
dc.authorscopusid57202192766en_US


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