Improving accuracy of local geoid model using machine learning approaches and residuals of GPS/levelling geoid height-Reference-Cited by-同舟云学术

Improving accuracy of local geoid model using machine learning approaches and residuals of GPS/levelling geoid height

Published:2021-08-27 Issue: Volume: Page:1-14
ISSN:0039-6265
Container-title:Survey Review
language:en
Short-container-title:Survey Review

Author:

Kaloop Mosbeh R.¹²³^ORCID,Pijush Samui⁴,Rabah Mostafa⁵,Al-Ajami Hamad⁵⁶,Hu Jong Wan¹²,Zaki Ahmed⁷^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, Incheon National University, Incheon, Korea

2. Incheon Disaster Prevention Research Center, Incheon National University, Incheon, Korea

3. Public Works and Civil Engineering Department, Mansoura University, Mansoura, Egypt

4. Department of Civil Engineering, National Institute of Technology Patna, Bihar, India

5. Civil Engineering Department, Benha University, Benha, Egypt

6. Training Authority, Public Authority for Applied Education and Training, Kuwait, Kuwait

7. Civil Engineering Department, Delta University for Science and Technology, Gamasa, Egypt

Funder

National Demand Customized Life Safety R&D Project

Korean Ministry of Interior and Safety

Publisher

Informa UK Limited

Subject

Earth and Planetary Sciences (miscellaneous),Computers in Earth Sciences,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/00396265.2021.1970918

Reference58 articles.

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4. Multivariate Adaptive Regression Splines (MARS) approach to blast-induced ground vibration prediction

5. Bi, J. and Bennett, K. 2003. Regression error characteristic curves. In Proceedings, Twentieth International Conference on Machine Learning. Available from https://www.aaai.org/Papers/ICML/2003/ICML03-009.pdf [accessed 22 June 2021].

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