DISPLACEMENT DETECTION IN GEODETIC NETWORK USING NEURAL NETWORK

Finding an optimal configuration is one the most important steps in the design and establishing a deformation monitoring network. In order to design an optimal geodetic network, Grafarend (1974) proposed a 4-order solution to the optimisation problem. Zero-Order Design (ZOD) to seek an optimum datum for the network; First-Order Design (FOD) to find an optimum configuration to the network by choosing the best possible positions for the net points; Second-Order Design (SOD) to deal with observations weight and choose the optimal one, and THird-Order Design (THOD) to improve an existing network by establishing a new net point and performing new observations. In this paper we propose a new method to detect deformation of geodetic network points by using neural networks. In this method we do not need to use control points and do some conventional tests in classical deformation detection methods. The proposed method have been done at two epochs distance and angles observation and result illustrated that method can determined displacement by 0.1mm accurately.