Staff Members - Benha University
): " Using Advanced Soft Computing Techniques for Local Geoid Model Estimation and Evaluation”
Marine Georesources & Geotechnology, • 2017
Publication Information
Authors
M. Kaloop M. Rabah, Jong Wan Hu1 and Ahmed Zaki
Keywords
Not Available
Journal
Marine Georesources & Geotechnology,
Publisher
Taylor & Francis Press.
Volume
Not Available
Issue
Not Available
Pages
Not Available
publication.type
International
Paper Link
Not Available
Supplementary Materials
Not Available
Abstract
This study aims at evaluating the global geoid model for a regional shoreline fitting using advanced soft
computing techniques and global navigation satellite system/leveling measurements. Artificial neural
networks, fuzzy logic, and least square support vector machine models are developed and used to fit the
global geoid model for the north coastal Egyptian line. In addition, a novel estimation geoid model is
designed and evaluated based on the latest global geoid models. The results of the three estimation
models show that they can be used to correct the shoreline geoid model, in terms of root mean square
error that ranges from 1.7 to 8.5 cm. Moreover, it is found that the least square vector machine model is a
competitive approach with certain advantage in solving complex problems represented by missing data
computing techniques and global navigation satellite system/leveling measurements. Artificial neural
networks, fuzzy logic, and least square support vector machine models are developed and used to fit the
global geoid model for the north coastal Egyptian line. In addition, a novel estimation geoid model is
designed and evaluated based on the latest global geoid models. The results of the three estimation
models show that they can be used to correct the shoreline geoid model, in terms of root mean square
error that ranges from 1.7 to 8.5 cm. Moreover, it is found that the least square vector machine model is a
competitive approach with certain advantage in solving complex problems represented by missing data