Tehnički vjesnik, Vol. 24 No. 2, 2017.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20151116094353
Application of artificial neural networks to height transformation
Mustafa Yilmaz
; Afyon Kocatepe University, Faculty of Engineering, Department of Geomatics Engineering ANS Campus, TR-03200
Bayram Turgut
; Afyon Kocatepe University, Faculty of Engineering, Department of Geomatics Engineering ANS Campus, TR-03200
Mevlut Gullu
; Afyon Kocatepe University, Faculty of Engineering, Department of Geomatics Engineering ANS Campus, TR-03200
Ibrahim Yilmaz
; Afyon Kocatepe University, Faculty of Engineering, Department of Geomatics Engineering ANS Campus, TR-03200
Sažetak
The vertical positioning has two indispensable constituents: the height and the relevant reference surface. The definition of the height differs according to the appointed reference surface. Global Navigation Satellite Systems (GNSS) ensure ellipsoidal heights relative to a geodetic reference ellipsoid surface. However, many field applications require heights that are related to a physically meaningful surface (e.g. the geoid). Such physically meaningful heights often provided in terms of orthometric heights. The geoid undulation is the relation between the ellipsoidal and orthometric heights. The ellipsoidal heights can be transformed to orthometric heights via two principal approaches: a gravimetric geoid model, and geometrical interpolation between geoid undulations where GNSS observations have been collocated with benchmarks. The purpose of this study is investigating the applicability of a back propagation artificial neural network as a height transformation tool.
Ključne riječi
back propagation artificial neural networks; ellipsoidal height; orthometric height
Hrčak ID:
179854
URI
Datum izdavanja:
14.4.2017.
Posjeta: 1.799 *