Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models-Reference-Cited by-同舟云学术

Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models

Published:2022-06-30 Issue:27 Volume:37 Page:14963-14982
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Wahla Saadia Sultan¹²³,Kazmi Jamil Hasan²,Sharifi Alireza⁴,Shirazi Safdar Ali⁵,Tariq Aqil⁶⁷^ORCID,Joyell Smith Hayley⁸

Affiliation:

1. Department of Geography, Government Post-Graduate College for Women, Bahawalnagar, Pakistan

2. University of Karachi, Karachi, Sindh, Pakistan

3. Department of Anthropology, University of Georgia, Athens, GA, USA

4. Department of Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

5. University of the Punjab, Lahore, Punjab, Pakistan

6. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

7. Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Mississippi State, MS, USA

8. Warnell School of Forestry and Natural Resources, Forestry Hydrology Lab, University of Georgia, Athens, GA, USA

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2022.2093411

Reference73 articles.

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2. Accuracy assessment of a low-cost UAV derived digital elevation model (DEM) in a highly broken and vegetated terrain

3. Simulation of the impacts of land-use change on surface runoff of Lai Nullah Basin in Islamabad, Pakistan

4. Climate change communicators’ carbon footprints affect their audience’s policy support

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