DEVELOPING A LOCAL GEOID MODEL FOR EGYPT USING MACHINE LEARNING ALGORITHMS | ||||
| Journal of Al-Azhar University Engineering Sector | ||||
| Volume 19, Issue 72, July 2024, Page 102-118 PDF (940.04 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/auej.2024.254838.1517 | ||||
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| Authors | ||||
Salah Shokry Alsadany  ; Essam Mohamed Fawaz; Mohamed Elshewy ; Ahmed Mohamed Hamdy Ibrahim
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| Civil Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt | ||||
| Abstract | ||||
| This research aims at making use of advanced Machine Learning Algorithms (MLAs) with a view to developning a precise geoid model for Egypt. Being an equipotential surface of the earth's gravity field, geoid plays a crucial role in various geodetic applications. Throughout this study, state-of-the-art Machine Learning Algorithms are employed to address the limitations of conventional geoid modeling approaches. The research methodology involves evaluating the performance of eight Global Geopotential Models(GGMs), namely EGM2008, EIGEN-6C, EIGEN-6C2, EIGEN-6C4, EIGEN-6C3stat, SGG-UGM-1, XGM2019e_2159 and SGG-UGM-2 to choose the suitable GGM that for the study area, i.e. Egypt. MLAs, such as Linear Regression, Support Vector Machine, Random Forest, and Extra Trees, are then applied to train a model capable of determinig the intricate relationships between the input features and the geoid undulations. The study findings conclude that XGM2019e_2159 emerges as the optimal GGM for Egyptian territories, since it has yielded a standard deviation of 0.36 m. Notable enhancements in the local geoid model are observed with the application of the Extra Trees algorithm, which has yielded a standard deviation of 0.11 m. Special Issue of AEIC 2024 (Civil Engineering Session) | ||||
| Keywords | ||||
| Machine learning; Random Forest; equipotential surface; geoid undulations; local geoid | ||||
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