Computing Local Geoid Model Using DTM and GPS Geodetic Points. Case Study: Mejez El Bab-Tunisia

Abstract

Different methods have been deployed to compute the geoid, the altimetry reference for surveying applications. One of their main goals is to allow the use of GPS (Global Positioning System) or GNSS heights, which are related to an ellipsoid and therefore must be corrected. Some of these methods are accurate but quite heavy as developed by [1], but one of them is easy to use while giving very good results in a local system: some mm for a 10 × 10 km 2 area developed by [2] [3]. In our study, we have used software called "Géoide Program", previously used at the CERN in Switzerland and set up by [4], which they complete this software allowing a parameterization of general data to provide results in a general system. Then, tests have shown the way to optimize computations without any loss of accuracy. For our computations we use gridded of geodetic heights, from Lambert or WGS 84 datum's, DTM (Digital Terrain Model) and leveled GPS points. To obtain these results, components of the vertical deflection are computed for every point on the grid, deduced from the attraction exerted by the mass Model. Then, geodetic heights are computed by an incremental way from an arbitrary reference. Once the calculation is performed, the geodetic height of any point located in the modelled area can be interpolated. The variations of parameters (mainly size and increments of the DTM and of the modeled area, and ground density) have shown that they do not play a significant role although DTM must be large enough to take into account an important area around a selected zone. However, the choice of the levelled GPS points is primordial. We have performed tests with real data concerning Mejez El Bab zone, in north of Tunisia. Nevertheless, for a few hundreds of square kilometers area, and just by using a DTM and a few levelled GPS points, this method provides results that look

Emotional characteristics and time series analysis of Internet public opinion participants based on emotional feature words

“…Therefore, the evolution analysis of public opinion in this article mainly includes two steps: (1) documents are divided according to discrete-time intervals and (2) documents after segmentation are analyzed by DTM model. 22,23…”

“…Therefore, the evolution analysis of public opinion in this article mainly includes two steps: (1) documents are divided according to discrete-time intervals and (2) documents after segmentation are analyzed by DTM model. 22,23 Based on time-slicing of sentiment values, consider the emotion value as a feature of the slice. First, calculate the absolute value of the two data sentiment value differences, and sort them in descending order to select the time point that meets the demand.…”