Enhancing a convolutional neural network model for land subsidence susceptibility mapping using hybrid meta-heuristic algorithms-Reference-Cited by-同舟云学术

Enhancing a convolutional neural network model for land subsidence susceptibility mapping using hybrid meta-heuristic algorithms

Published:2023-09 Issue: Volume:277 Page:104350
ISSN:0166-5162
Container-title:International Journal of Coal Geology
language:en
Short-container-title:International Journal of Coal Geology

Author:

Jafari Ali,Alesheikh Ali Asghar,Rezaie Fatemeh,Panahi Mahdi,Shahsavar Shiva,Lee Moung-Jin,Lee Saro

Funder

Korea Environment Institute

Korea Institute of Geoscience and Mineral Resources

National Research Foundation of Korea

Ministry of the Interior and Safety

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Subject

Stratigraphy,Economic Geology,Geology,Fuel Technology

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4. A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility;Arabameri;Sci. Total Environ.,2020

5. Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation;Arabameri;J. Environ. Manag.,2021

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