Imperial College London

MrJakeLever

Faculty of EngineeringDepartment of Earth Science & Engineering

Research Postgraduate
 
 
 
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Contact

 

j.lever20

 
 
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Location

 

Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

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3 results found

Lever J, Arcucci R, 2022, Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting, Journal of Computational Social Science, Vol: 5, Pages: 1427-1465, ISSN: 2432-2717

The intensity of wildfires and wildfire season length is increasing due to climate change, causing a greater threat to the local population. Much of this population are increasingly adopting social media, and sites like Twitter are increasingly being used as a real-time human-sensor network during natural disasters; detecting, tracking and documenting events. The human-sensor concept is currently largely omitted by wildfire models, representing a potential loss of information. By including Twitter data as a source in our models, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This paper implements machine learning in a wildfire prediction model, using social media and geophysical data sources with Sentiment Analysis to predict wildfire characteristics with high accuracy. We also use wildfire-specific attributes to predict online social dynamics, as this has been shown to be indicative of localised disaster severity. This may be useful for disaster management teams in identifying areas of immediate danger. We combine geophysical satellite data from the Global Fire Atlas with social data provided by Twitter. We perform data collection and subsequent analysis & visualisation, and compare regional differences in online social sentiment expression. Following this, we compare and contrast different machine learning models for predicting wildfire attributes. We demonstrate social media is a predictor of wildfire activity, and present models which accurately model wildfire attributes. This work develops the concept of the human sensor in the context of wildfires, using users’ Tweets as noisy subjective sentimental accounts of current localised conditions. This work contributes to the development of more socially conscious wildfire models, by incorporating social media data into wildfire prediction and modelling.

Journal article

Lever J, Arcucci R, 2022, Towards Social Machine Learning for Natural Disasters, Computational Science – ICCS 2022 22nd International Conference, London, UK, June 21–23, 2022, Proceedings, Part III, Publisher: Springer, Pages: 756-769, ISBN: 9783031087561

The four-volume set LNCS 13350, 13351, 13352, and 13353 constitutes the proceedings of the 22ndt International Conference on Computational Science, ICCS 2022, held in London, UK, in June 2022.* The total of 175 full papers and 78 short ...

Book chapter

Lever J, Arcucci R, Cai J, 2022, Social Data Assimilation of Human Sensor Networks for Wildfires, 15th ACM International Conference on Pervasive Technologies Related to Assistive Environments (PETRA), Publisher: ASSOC COMPUTING MACHINERY, Pages: 455-462

Conference paper

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