A Framework Using Federated Learning for IoT-Based Forest Fire Prediction-Reference-Cited by-同舟云学术

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A Framework Using Federated Learning for IoT-Based Forest Fire Prediction

Published:2023-11-28 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)
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Author:

Purcell Richard¹,Naik Kshirasagar²,Lung Chung-Horng³,Zaman Marzia⁴,Sampalli Srinivas¹,Mutakabbir Abdul³

Affiliation:

1. Dalhousie University,Faculty of Computer Science,Halifax,NS,Canada

2. Electrical and Computer Engineering, University of Waterloo,Waterloo,ON,Canada

3. Systems and Computer Engineering, Carleton University,Ottawa,ON,Canada

4. Research and Development Cistel Technology,Ottawa,ON,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10346030/10346031/10346044.pdf?arnumber=10346044

Reference17 articles.

1. Trends in Canadian Forest Fires, 1959–2019;Murphy,2023

2. The Canadian Wildland Fire Information System;Lee,1995

3. Weather Guide for the Canadian Forest Fire Danger Rating System;Lawson;Natural Resources Canada, Cana-dian Forest Service, Northern Forestry Centre, Edmonton,2008

4. Fire Information for Resource Man-agement System (FIRMS),2023

5. From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors

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. A Federated Learning Framework Based on Spatio-Temporal Agnostic Subsampling (STAS) for Forest Fire Prediction;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

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