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paper/paper.md

@@ -34,7 +34,7 @@ A major effect of climate change today is the increased frequency and intensity
 
 There has been significant traction in the use of computational models to study wildfires. Historically, much work has focused on accurately modeling the spread of wildfires. While a lot of older methods were primarily done using physics-based methods  [@rothermel1972mathematical; @Andrews_1986] – with Rothermel being one of the most popular, as well the one we utilize in our package – newer methods rely on machine learning and other data-driven approaches, incorporating a higher diversity of features  [@https://doi.org/10.1002/eap.1898; @Diao2020;@ross2021being].
 
-Reinforcement learning (RL), a subdomain of artificial intelligence where models learn through interaction with their environment – has also been increasingly used in the context of wildfires. In combination with other traditional statistical methods and computer vision [@ganapathi2018using; @satelliteimages2017], RL has been applied to both the surveillance and monitoring of wildfires [@Julian2019; @altamimi2022large; @9340340]. An area where there has been little work in regards to RL is wildfire evacuation. Understanding the effective approaches for evacuating populated areas during wildfires is a key safety concern during these events [@KULIGOWSKI2021103129, @McCaffrey_2017]. As a result, work has been done to better model traffic during wildfire evacuation scenarios [@Pel, @doi:10.1061/JTEPBS.0000221], and agent-based evacuation simulations have been used for not only wildfires but also other natural disasters like tsunamis [@BELOGLAZOV2016144, @WANG201686]. RL has been previously identified as a potentially helpful tool during evacuation operations [@rempel_shiell_2022] and has been used to model evacuation during electrical substation fires [@10.1063/5.0209018]. The application of RL techniques to the wildfire evacuation task could thus prove beneficial. 
+Reinforcement learning (RL), a subdomain of artificial intelligence where models learn through interaction with their environment – has also been increasingly used in the context of wildfires. In combination with other traditional statistical methods and computer vision [@ganapathi2018using; @satelliteimages2017], RL has been applied to both the surveillance and monitoring of wildfires [@Julian2019; @altamimi2022large; @9340340]. An area where there has been little work in regards to RL is wildfire evacuation. Understanding the effective approaches for evacuating populated areas during wildfires is a key safety concern during these events [@KULIGOWSKI2021103129; @McCaffrey_2017]. As a result, work has been done to better model traffic during wildfire evacuation scenarios [@Pel; @doi:10.1061/JTEPBS.0000221], and agent-based evacuation simulations have been used for not only wildfires but also other natural disasters like tsunamis [@BELOGLAZOV2016144; @WANG201686]. RL has been previously identified as a potentially helpful tool during evacuation operations [@rempel_shiell_2022] and has been used to model evacuation during electrical substation fires [@10.1063/5.0209018]. The application of RL techniques to the wildfire evacuation task could thus prove beneficial. 
 
 Given the growing interest in studying wildfires through a computational lens, there have been developments in simulators for wildfires. A lot of open-source software focus on providing a visualization of wildfire spread [@cellular_automata; @forest_fire]. The most relevant piece of work to our paper are SimFire and SimHarness, which provide a system for wildland fire spread and a way for appropriate mitigation strategy responses via RL [@tapley2023reinforcementlearningwildfiremitigation]. Nonetheless, the focus is still on wildfire surveillance and mitigation, not on the task of evacuation.