Affiliation:
1. Department of Geomatic Engineering, Faculty of Civil Engineering, Yildiz Technical University, Esenler, Istanbul 34220, Turkey
2. Department of Geomatic Engineering, Faculty of Engineering, Nigde Omer Halisdemir University, Nigde 51245, Turkey
Abstract
Digital 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.
Subject
Information Systems and Management,Computer Networks and Communications,Modeling and Simulation,Control and Systems Engineering,Software
Reference40 articles.
1. Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density;Chaplot;Geomorphology,2006
2. Comparative investigation of parallel spatial interpolation algorithms for building large-scale digital elevation models;Tu;PeerJ Comput. Sci.,2020
3. Yan, L., Tang, X., and Zhang, Y. (2021). High Accuracy Interpolation of DEM Using Generative Adversarial Network. Remote Sens., 13.
4. Longley, P.A., Goodchild, M.F., Maguire, D.J., and Rhind, D.W. (2005). Geographical Information Systems: Principles, Techniques, Management and Applications, John Wiley & Sons. [2nd ed.].
5. Accuracy Assessment of Digital Elevation Models based on Approximation Theory;Hu;Photogramm. Eng. Remote Sens.,2009
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Analysis of a Backpropagation Neural Network Training Algorithm for EEG-Based Drowsiness Identification;2023 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET);2023-11-15