Application of novel hybrid model for land subsidence susceptibility mapping-Reference-Cited by-同舟云学术

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Application of novel hybrid model for land subsidence susceptibility mapping

Published:2022-10-19 Issue: Volume: Page:
ISSN:0072-1050
Container-title:Geological Journal
language:en
Short-container-title:Geological Journal

Author:

Shen ZhongJie¹,Santosh M.²³^ORCID,Arabameri Alireza⁴

Affiliation:

1. Xijing University Xi'an China

2. School of Earth Sciences and Resources China University of Geosciences Beijing Beijing China

3. Department of Earth Sciences University of Adelaide Adelaide South Australia Australia

4. Department of Geomorphology Tarbiat Modares University Tehran Iran

Publisher

Wiley

Subject

Geology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/gj.4603

Reference116 articles.

1. Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions

2. Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis

3. A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility

4. Arlot S. &Genuer R.(2014).Analysis of purely random forests bias. arXiv preprint arXiv:1407.3939.

5. Geospatial subsidence hazard modelling at Sterkfontein Caves;Ashraf H.;South African Journal of Geomatics,2015

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1. Urban ground subsidence monitoring and prediction using time-series InSAR and machine learning approaches: a case study of Tianjin, China;Environmental Earth Sciences;2024-08

2. Landslide Susceptibility Assessment Considering Time-Varying of Dynamic Factors;Natural Hazards Review;2024-08

3. Urban Ground Subsidence Monitoring and Prediction Using Time-Series InSAR and Machine Learning Approaches: A Case Study of Tianjin, China;2024-05-23

4. An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning;Environmental Monitoring and Assessment;2024-05-06

5. Land subsidence prediction in coal mining using machine learning models and optimization techniques;Environmental Science and Pollution Research;2024-04-19

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