Comparison of statistical and machine learning approaches in land subsidence modelling-Reference-Cited by-同舟云学术

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Comparison of statistical and machine learning approaches in land subsidence modelling

Published:2021-06-07 Issue: Volume: Page:1-21
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Rafiei Sardooi Elham¹^ORCID,Pourghasemi Hamid Reza²^ORCID,Azareh Ali³^ORCID,Soleimani Sardoo Farshad¹,Clague John J.⁴^ORCID

Affiliation:

1. Department of Ecological Engineering, Faculty of Natural Recourses, University of Jiroft, Kerman, Iran

2. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

3. Department of Geography, University of Jiroft, Kerman, Iran

4. Department of Earth Sciences, Simon Fraser University, Institute for Quaternary Research, Burnaby, British Columbia, Canada

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2021.1933211

Reference104 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. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)

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

4. Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models

5. Modeling the Impact of Climate Change and Land Use Change Scenarios on Soil Erosion at the Minab Dam Watershed

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