چكيده لاتين
The geoid is one of the equipotential surfaces of the Earthʹs gravity field, which approximates the mean sea level (MSL) in the best possible way (according to the theory of the least squares method). This level is known as the height base (elevation datum - physical form) of the earth and is used in civil and construction projects to determine heights. Therefore, accurate modeling of this level is very important in surveying and geodesy. There are different methods for geoid modeling. These methods include: geometrical method, geoid determination by satellite method, gravimetric methods and geoid determination using GPS/leveling. Each of these methods has advantages and disadvantages. However, nowadays, due to the expansion of GNSS networks and also accurate leveling in the stations of these networks, the use of GPS/Leveling method has gained more acceptances. In this method, having the orthometric and normal heights, the height of the geoid can be calculated.
In recent years, machine learning methods have been used for local geoid height modeling. The main advantage of machine learning methods is simple and fast calculations, as well as high accuracy and precision in the results. Therefore, in this thesis, the generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR) models have been used to estimate the local geoid height in the Iranian region. In order to evaluate the proposed models, 31 and 26 GPS/Leveling stations have been used in the Iranian plateau in central Alborz network and northwest network in three different scenarios. In these three scenarios, considering 2, 4 and 7 test stations, the accuracy of the models have been investigated. Also, the results of the machine learning models in the test stations have been compared with the IRG2016 and EGM2008 models. In the first scenario, where the number of training stations is more than the other two scenarios, the RMSE of ANFIS, SVR, GRNN, IRG2016 and EGM2008 models in the central Alborz network is 27/35, 63/69, 62/35, 25/26, and 63.20 cm, respectively. For the northwest network, the RMSE is 3.94, 83.27, 93.11, 23.05 and 45.65 cm, respectively. In this scenario and in both studied networks, the error of the ANFIS model is lower than the other two models. However, in scenarios two and three, the error of the ANFIS model increased, but the error of the SVR and GRNN models decreased. This shows that the SVR and GRNN models provide higher accuracy with less input data. The averaged RMSE of IRG2016 and EGM2008 models in two studied networks is 26.15 and 54.42 cm, respectively. Therefore, the local model of IRG2016 has been more accurate than the global model. But the comparison of the IRG2016 model with the ANFIS model presented in this thesis shows that the new model can provide higher accuracy in estimating the spatial varations of the geoid height if there are more GPS/Leveling station observations with a more uniform distribution.
