Approximation of the process of changes in deformation of land surface using artificial neural networks

¹ University of Zielona Góra, Institute of Building Engineering, Faculty of Civil Engineering, Architecture and Environmental Engineering, 1 Szafrana St., 65-516 Zielona Góra, Poland
² University of Science and Technology, Faculty of Civil and Environmental Engineering and Architecture, 7 prof. S. Kaliski St., 85-796 Bydgoszcz, Poland

^* Corresponding author: m.mrowczynska@ib.uz.zgora.pl

Abstract

Artificial neural networks are an interesting method for modelling phenomena, including spatial phenomena, which are difficult to describe with known mathematical models. The properties of neural networks enable their practical application for solving such problems as: approximation, interpolation, identification and classification of patterns, compression, prediction, etc. The article presents the use of multilayer feedforward artificial neural networks for describing the process of changes in land surface deformation in the area of the Legnica-Głogów Copper Mining Centre, located in the southern part of the Fore Sudetic Monocline. Results provided by geodesic monitoring, which consists of land surveying and interpreting data obtained in this way, are undoubtedly significant in terms of identifying the impact of mining on the land surface the results of measurements carried out by precise levelling in the years 19672014 were used to determine changes in land deformation in the Legnica-Głogów Copper Mining Centre. The concept of a flexible reference system was used to assess the stability of points in the measurement and control network stabilized in order to determine vertical displacements. However, the reference system itself was identified on the basis of the critical value of the increment of the square of the norm of corrections to the observations.