Three novel cost-sensitive machine learning models for urban growth modelling-Reference-Cited by-同舟云学术

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Three novel cost-sensitive machine learning models for urban growth modelling

Published:2024-01 Issue:1 Volume:39 Page:
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Ahmadlou Mohammad¹^ORCID,Karimi Mohammad¹,Sammen Saad Sh.²^ORCID,Alsafadi Karam³^ORCID

Affiliation:

1. GIS Department, Geodesy and Geomatics Faculty, K.N. Toosi University of Technology, Tehran, Iran

2. Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah, Iraq

3. Department of Geography, Faculty of Arts and Humanities, Damascus University, Damascus, Syria

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2024.2353252

Reference55 articles.

1. The use of maximum entropy and ecological niche factor analysis to decrease uncertainties in samples for urban gain models

2. A new framework to deal with the class imbalance problem in urban gain modeling based on clustering and ensemble models

3. Towards ensuring sustainable urban growth and development in Nigeria: challenges and strategies;Ayedun C;Business Manag Dynm,2011

4. The spatio-temporal dynamics of urban growth and population in metropolitan regions of Iran

5. EMERGING PATTERN OF URBANIZATION AND THE CONTRIBUTION OF MIGRATION IN URBAN GROWTH IN INDIA

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of daily leaf wetness duration using multi-step machine learning;Computers and Electronics in Agriculture;2024-09

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