Comparison of Different Parameters of Feedforward Backpropagation Neural Networks in DEM Height Estimation for Different Terrain Types and Point Distributions

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dc.authoridSEN, ALPER/0000-0002-7236-6701
dc.authoridgumus, kutalmis/0000-0003-3114-8449
dc.contributor.authorSen, Alper
dc.contributor.authorGumus, Kutalmis
dc.date.accessioned2024-11-07T13:32:03Z
dc.date.available2024-11-07T13:32:03Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractDigital Elevation Models (DEMs) are commonly used for environment, engineering, and architecture-related studies. One of the most important factors for the accuracy of DEM generation is the process of spatial interpolation, which is used for estimating the height values of the grid cells. The use of machine learning methods, such as artificial neural networks for spatial interpolation, contributes to spatial interpolation with more accuracy. In this study, the performances of FBNN interpolation based on different parameters such as the number of hidden layers and neurons, epoch number, processing time, and training functions (gradient optimization algorithms) were compared, and the differences were evaluated statistically using an analysis of variance (ANOVA) test. This research offers significant insights into the optimization of neural network gradients, with a particular focus on spatial interpolation. The accuracy of the Levenberg-Marquardt training function was the best, whereas the most significantly different training functions, gradient descent backpropagation and gradient descent with momentum and adaptive learning rule backpropagation, were the worst. Thus, this study contributes to the investigation of parameter selection of ANN for spatial interpolation in DEM height estimation for different terrain types and point distributions.
dc.identifier.doi10.3390/systems11050261
dc.identifier.issn2079-8954
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85160111162
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/systems11050261
dc.identifier.urihttps://hdl.handle.net/11480/15190
dc.identifier.volume11
dc.identifier.wosWOS:000997185200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSystems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectDEM generation
dc.subjectspatial interpolation
dc.subjectartificial neural networks
dc.subjectgradient optimization
dc.titleComparison of Different Parameters of Feedforward Backpropagation Neural Networks in DEM Height Estimation for Different Terrain Types and Point Distributions
dc.typeArticle

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