Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@inproceedings{Lever:2022:10.1145/3529190.3534735,
author = {Lever, J and Arcucci, R and Cai, J},
doi = {10.1145/3529190.3534735},
pages = {455--462},
title = {Social Data Assimilation of Human Sensor Networks for Wildfires},
url = {http://dx.doi.org/10.1145/3529190.3534735},
year = {2022}
}
TY - CPAPER
AB - We present an implementation of a human sensor network in the context of wildfires. A human sensor network can be thought of as a socially nuanced abstraction of a physical sensing model, where social media users are considered noisy remote sensors with variable reliability and location. This allows real-time social modelling of physical events. We apply this concept to data collected from Twitter & Reddit in the context of California wildfires, performing sentimental & topical analysis over the period of a wildfire season to extract themes, sentiments and discussions. We assimilate this social media data in a predictive model trained by machine learning approaches for time series. Both Long Short Term Memory (LSTM) & AutoRegressive Integrated Moving Average (ARIMA) models are employed. We assimilate the human sensor networks, to overcome the limitations & biases exhibited by individual social media platform demographics. We implement Optimal Interpolation and Ensemble Kalman Filter architectures on our models & data. Finally we compare and evaluate performance, and discuss how these implementations could benefit current wildfire models.
AU - Lever,J
AU - Arcucci,R
AU - Cai,J
DO - 10.1145/3529190.3534735
EP - 462
PY - 2022///
SP - 455
TI - Social Data Assimilation of Human Sensor Networks for Wildfires
UR - http://dx.doi.org/10.1145/3529190.3534735
ER -