An improved GM(1,3) model combining terrain factors and neural network error correction for urban land subsidence prediction
Author:
Affiliation:
1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
2. Department of Surveying and Mapping, The Second Surveying and Mapping Institute of Zhejiang Province, Hangzhou, China
Funder
Basic Public Welfare Research Program Of Zhejiang Province, China
Publisher
Informa UK Limited
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://www.tandfonline.com/doi/pdf/10.1080/19475705.2020.1716860
Reference38 articles.
1. Abidin HZ, Andreas H, Gumilar I, Sidiq TP, Fukuda Y. 2013. On the roles of geospatial information for risk assessment of land subsidence in urban areas of Indonesia, Intelligent systems for crisis management. Berlin, Germany: Springer, p. 277–288.
2. Bahar R, Akchiche M, Derriche Z. 1997. Analyse des tassements en surface induits par le creusement d’une galerie dans des marnes. XIVe Congrès international de mécanique des sols et travaux de fondations, Hambourg. 2:1211–1214.
3. Arbuscular mycorrhizal fungi alleviate root damage stress induced by simulated coal mining subsidence ground fissures
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