Imperial College London
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DrRossellaArcucci

Faculty of Engineering, Department of Earth Science & Engineering

Senior Lecturer in Data Science and Machine Learning
 
 
 
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Contact

 

r.arcucci Website

 
 
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Location

 

Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Lever:2022:10.1007/s42001-022-00174-8,
author = {Lever, J and Arcucci, R},
doi = {10.1007/s42001-022-00174-8},
journal = {Journal of Computational Social Science},
pages = {1427--1465},
title = {Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting},
url = {http://dx.doi.org/10.1007/s42001-022-00174-8},
volume = {5},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Lever,J
AU  - Arcucci,R
DO  - 10.1007/s42001-022-00174-8
EP  - 1465
PY  - 2022///
SN  - 2432-2717
SP  - 1427
TI  - Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting
T2  - Journal of Computational Social Science
UR  - http://dx.doi.org/10.1007/s42001-022-00174-8
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000828958700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://link.springer.com/article/10.1007/s42001-022-00174-8
UR  - http://hdl.handle.net/10044/1/99698
VL  - 5
ER  -
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