Estimation of soil erodability parameters based on different machine algorithms integrated with remote sensing techniques

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dc.authoridAlaboz, Pelin/0000-0001-7345-938X
dc.authoridAksoy, Hasan/0000-0003-1980-3834
dc.contributor.authorSaygin, F.
dc.contributor.authorAksoy, H.
dc.contributor.authorAlaboz, P.
dc.contributor.authorBirol, M.
dc.contributor.authorDengiz, O.
dc.date.accessioned2025-03-23T19:42:16Z
dc.date.available2025-03-23T19:42:16Z
dc.date.issued2024
dc.departmentSinop Üniversitesi
dc.description.abstractErosion causes significant damage to life and nature every year; therefore, controlling erosion is of great importance. Therefore, maintaining the balance between soil, plants, and water plays a vital role in controlling erosion. Aim of this study was to estimate some erodability parameters (structural stability index-SSI, aggregate stability-AS, and erosion ratio-ER) with indices and reflectance obtained via TripleSat satellite imagery using machine learning algorithms (support vector regression-SVR, artificial neural network-ANN, and K-nearest neighbors-KNN) in Samsun Province, Vezirkopru, Turkiye. Various interpolation methods (inverse distance weighting-IDW, radial basis function-RBF, and kriging) were also used to create spatial distribution maps of the study area for observed and predicted values. Estimates were made using NDVI, SAVI, and ASVI indices obtained from satellite images and NIR reflectance. Accordingly, the ANN algorithm yielded the lowest MAE (2.86%), MAPE (9.46%), and highest R2 (0.82) for SSI estimation. For AS and ER estimation, SVR had the highest predictive accuracy. Given the RMSE values in spatial distribution maps for observed and estimated values (SSI 7.861-7.248%, AS 14.485-14.536%, and ER 4.919-3.742%), the highest predictive accuracy was obtained with kriging. Thus, it was concluded that erosion parameters can be successfully estimated with reflectance and index values obtained from satellite images using SVR and ANN algorithms, and low-error distribution maps can be created using the kriging method.
dc.identifier.doi10.1007/s13762-024-05574-z
dc.identifier.endpage9540
dc.identifier.issn1735-1472
dc.identifier.issn1735-2630
dc.identifier.issue15
dc.identifier.scopus2-s2.0-85189456794
dc.identifier.scopusqualityQ1
dc.identifier.startpage9527
dc.identifier.urihttps://doi.org/10.1007/s13762-024-05574-z
dc.identifier.urihttps://hdl.handle.net/11486/6749
dc.identifier.volume21
dc.identifier.wosWOS:001198020400007
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofInternational Journal of Environmental Science and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250323
dc.subjectSoil properties
dc.subjectErodibility factors
dc.subjectMachine-learning algorithm
dc.subjectRemote sensing
dc.subjectKriging
dc.titleEstimation of soil erodability parameters based on different machine algorithms integrated with remote sensing techniques
dc.typeArticle

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