Igniting Precision: Amplifying Wildfire Prediction in Diverse Regions via Teacher-Student Model Fusion-Reference-Cited by-同舟云学术

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Igniting Precision: Amplifying Wildfire Prediction in Diverse Regions via Teacher-Student Model Fusion

Published:2023-12-15 Issue: Volume:5 Page:528-535
ISSN:
Container-title:2023 International Conference on Machine Learning and Applications (ICMLA)
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Author:

Lindemann Michael¹,DeMichele Kathleen²,Kapourchali Masoumeh Heidari²,Kapourchali Mohammad Heidari¹,Waigl Christine³,Trochim Erin⁴,Zhao Long⁵

Affiliation:

1. University of Alaska Anchorage,Department of Electrical Engineering,Anchorage,AK

2. University of Alaska Anchorage,Department of Computer Science and Engineering,Anchorage,AK

3. International Arctic Research Center, University of Alaska Fairbanks,Fairbanks,AK

4. Alaska Center for Energy and Power, University of Alaska Fairbanks,Fairbanks,AK

5. South Dakota School of Mines and Technology,Department of Electrical Engineering and Computer Science

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10459339/10459727/10459947.pdf?arnumber=10459947

Reference34 articles.

1. The age of alaskan wildfires;Sanford;Climate Central, Princeton,2015

2. Identification and characterization of forest fire risk zones leveraging machine learning methods;Balson;SMU Data Science Review,2021

3. Detecting high and low-intensity fires in Alaska using VIIRS I-band data: An improved operational approach for high latitudes

4. Use of Remote Sensing in Wildfire Management

5. Identification and modelling of forest fire severity and risk zones in the Cross – Niger transition forest with remotely sensed satellite data

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