diff --git a/paper/paper.bib b/paper/paper.bib index 0f54247..edf103c 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,299 +1,302 @@ @misc{towers_gymnasium_2023, - title = {Gymnasium}, - url = {https://zenodo.org/record/8127025}, - abstract = {An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)}, - urldate = {2023-07-08}, - author = {Towers, Mark and Terry, Jordan K. and Kwiatkowski, Ariel and Balis, John U. and Cola, Gianluca de and Deleu, Tristan and Goulão, Manuel and Kallinteris, Andreas and KG, Arjun and Krimmel, Markus and Perez-Vicente, Rodrigo and Pierré, Andrea and Schulhoff, Sander and Tai, Jun Jet and Shen, Andrew Tan Jin and Younis, Omar G.}, - month = mar, - year = {2023}, - doi = {10.5281/zenodo.8127026}, + title = {Gymnasium}, + url = {https://zenodo.org/record/8127025}, + abstract = {An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)}, + urldate = {2023-07-08}, + author = {Towers, Mark and Terry, Jordan K. and Kwiatkowski, Ariel and Balis, John U. and Cola, Gianluca de and Deleu, Tristan and Goulão, Manuel and Kallinteris, Andreas and KG, Arjun and Krimmel, Markus and Perez-Vicente, Rodrigo and Pierré, Andrea and Schulhoff, Sander and Tai, Jun Jet and Shen, Andrew Tan Jin and Younis, Omar G.}, + month = mar, + year = {2023}, + doi = {10.5281/zenodo.8127026} } @article{firetech, -author = {Xu, Ningzhe and Lovreglio, Ruggiero and Kuligowski, Erica and Cova, Thomas and Nilsson, Daniel and Zhao, Xilei}, -year = {2023}, -month = {01}, -pages = {}, -title = {Predicting and Assessing Wildfire Evacuation Decision-Making Using Machine Learning: Findings from the 2019 Kincade Fire}, -journal = {Fire Technology}, -doi = {10.1007/s10694-023-01363-1} + author = {Xu, Ningzhe and Lovreglio, Ruggiero and Kuligowski, Erica and Cova, Thomas and Nilsson, Daniel and Zhao, Xilei}, + year = {2023}, + month = {01}, + pages = {}, + title = {Predicting and Assessing Wildfire Evacuation Decision-Making Using Machine Learning: Findings from the 2019 Kincade Fire}, + journal = {Fire Technology}, + doi = {10.1007/s10694-023-01363-1} } @misc{tapley2023reinforcementlearningwildfiremitigation, - title={Reinforcement Learning for Wildfire Mitigation in Simulated Disaster Environments}, - author={Alexander Tapley and Marissa Dotter and Michael Doyle and Aidan Fennelly and Dhanuj Gandikota and Savanna Smith and Michael Threet and Tim Welsh}, - year={2023}, - eprint={2311.15925}, - archivePrefix={arXiv}, - primaryClass={cs.LG}, - url={https://arxiv.org/abs/2311.15925}, + title = {Reinforcement Learning for Wildfire Mitigation in Simulated Disaster Environments}, + author = {Alexander Tapley and Marissa Dotter and Michael Doyle and Aidan Fennelly and Dhanuj Gandikota and Savanna Smith and Michael Threet and Tim Welsh}, + year = {2023}, + eprint = {2311.15925}, + archiveprefix = {arXiv}, + primaryclass = {cs.LG}, + url = {https://arxiv.org/abs/2311.15925} } @article{KULIGOWSKI2021103129, -title = {Evacuation decision-making and behavior in wildfires: Past research, current challenges and a future research agenda}, -journal = {Fire Safety Journal}, -volume = {120}, -pages = {103129}, -year = {2021}, -note = {Fire Safety Science: Proceedings of the 13th International Symposium}, -issn = {0379-7112}, -doi = {https://doi.org/10.1016/j.firesaf.2020.103129}, -url = {https://www.sciencedirect.com/science/article/pii/S0379711220302204}, -author = {Erica Kuligowski}, -keywords = {Wildfires, Human behavior, Evacuation, Modeling, Bushfires, WUI fires}, -abstract = {Wildfires are becoming more common around the world, and households are frequently advised to evacuate when these fires threaten nearby communities. Effective evacuation requires an understanding of human behavior in wildfires, which is an area that needs further exploration. The purpose of this article is to present current research performed and data collected on evacuation decision-making and behavior during wildland-urban interface (WUI) fires, identify gaps in the research, and develop a future research plan for further data collection of important WUI fire evacuation topics. Research in this area can support developments of evacuation simulation models, and improvements in education programs, planning, decision-making, and design requirements for community-wide WUI fire evacuation.} + title = {Evacuation decision-making and behavior in wildfires: Past research, current challenges and a future research agenda}, + journal = {Fire Safety Journal}, + volume = {120}, + pages = {103129}, + year = {2021}, + note = {Fire Safety Science: Proceedings of the 13th International Symposium}, + issn = {0379-7112}, + doi = {10.1016/j.firesaf.2020.103129}, + url = {https://www.sciencedirect.com/science/article/pii/S0379711220302204}, + author = {Erica Kuligowski}, + keywords = {Wildfires, Human behavior, Evacuation, Modeling, Bushfires, WUI fires}, + abstract = {Wildfires are becoming more common around the world, and households are frequently advised to evacuate when these fires threaten nearby communities. Effective evacuation requires an understanding of human behavior in wildfires, which is an area that needs further exploration. The purpose of this article is to present current research performed and data collected on evacuation decision-making and behavior during wildland-urban interface (WUI) fires, identify gaps in the research, and develop a future research plan for further data collection of important WUI fire evacuation topics. Research in this area can support developments of evacuation simulation models, and improvements in education programs, planning, decision-making, and design requirements for community-wide WUI fire evacuation.} } @article{WANG201686, -title = {An agent-based model of a multimodal near-field tsunami evacuation: Decision-making and life safety}, -journal = {Transportation Research Part C: Emerging Technologies}, -volume = {64}, -pages = {86-100}, -year = {2016}, -issn = {0968-090X}, -doi = {https://doi.org/10.1016/j.trc.2015.11.010}, -url = {https://www.sciencedirect.com/science/article/pii/S0968090X15004106}, -author = {Haizhong Wang and Alireza Mostafizi and Lori A. Cramer and Dan Cox and Hyoungsu Park}, -keywords = {Agent-based modeling, Tsunami evacuation, Multimodal, Decision-making, Life safety}, -abstract = {This paper presents a multimodal evacuation simulation for a near-field tsunami through an agent-based modeling framework in Netlogo. The goals of this paper are to investigate (1) how the varying decisn time impacts the mortality rate, (2) how the choice of different modes of transportation (i.e., walking and automobile), and (3) how existence of vertical evacuation gates impacts the estimation of casualties. Using the city of Seaside, Oregon as a case study site, different individual decision-making time scales are included in the model to assess the mortality rate due to immediate evacuation right after initial earthquake or after a specified milling time. The results show that (1) the decision-making time (τ) and the variations in decision time (σ) are strongly correlated with the mortality rate; (2) the provision of vertical evacuation structures is effective to reduce the mortality rate; (3) the mortality rate is sensitive to the variations in walking speed of the evacuee population; and (4) the higher percentage of automobile use in tsunami evacuation, the higher the mortality rate. Following the results, this paper concludes with a description of the challenges ahead in agent-based tsunami evacuation modeling and simulation, and the modeling of complex interactions between agents (i.e., pedestrian and car interactions) that would arise for a multi-hazard scenario for the Cascadia Subduction Zone.} + title = {An agent-based model of a multimodal near-field tsunami evacuation: Decision-making and life safety}, + journal = {Transportation Research Part C: Emerging Technologies}, + volume = {64}, + pages = {86-100}, + year = {2016}, + issn = {0968-090X}, + doi = {10.1016/j.trc.2015.11.010}, + url = {https://www.sciencedirect.com/science/article/pii/S0968090X15004106}, + author = {Haizhong Wang and Alireza Mostafizi and Lori A. Cramer and Dan Cox and Hyoungsu Park}, + keywords = {Agent-based modeling, Tsunami evacuation, Multimodal, Decision-making, Life safety}, + abstract = {This paper presents a multimodal evacuation simulation for a near-field tsunami through an agent-based modeling framework in Netlogo. The goals of this paper are to investigate (1) how the varying decisn time impacts the mortality rate, (2) how the choice of different modes of transportation (i.e., walking and automobile), and (3) how existence of vertical evacuation gates impacts the estimation of casualties. Using the city of Seaside, Oregon as a case study site, different individual decision-making time scales are included in the model to assess the mortality rate due to immediate evacuation right after initial earthquake or after a specified milling time. The results show that (1) the decision-making time (τ) and the variations in decision time (σ) are strongly correlated with the mortality rate; (2) the provision of vertical evacuation structures is effective to reduce the mortality rate; (3) the mortality rate is sensitive to the variations in walking speed of the evacuee population; and (4) the higher percentage of automobile use in tsunami evacuation, the higher the mortality rate. Following the results, this paper concludes with a description of the challenges ahead in agent-based tsunami evacuation modeling and simulation, and the modeling of complex interactions between agents (i.e., pedestrian and car interactions) that would arise for a multi-hazard scenario for the Cascadia Subduction Zone.} } @article{BELOGLAZOV2016144, -title = {Simulation of wildfire evacuation with dynamic factors and model composition}, -journal = {Simulation Modelling Practice and Theory}, -volume = {60}, -pages = {144-159}, -year = {2016}, -issn = {1569-190X}, -doi = {https://doi.org/10.1016/j.simpat.2015.10.002}, -url = {https://www.sciencedirect.com/science/article/pii/S1569190X15001483}, -author = {Anton Beloglazov and Mahathir Almashor and Ermyas Abebe and Jan Richter and Kent Charles Barton Steer}, -keywords = {Wildfire, Evacuation planning, Dynamic factors, Model composition, Behaviour and risk modelling}, -abstract = {Wildfires cause devastation on communities, most significantly loss of life. The safety of at-risk populations depends on accurate risk assessment and emergency planning. Evacuation modelling and simulation systems are essential tools for such planning and decision making. During a wildfire evacuation, the behaviour of people is a key factor; what people do, and when they do it, depends heavily on the spatio-temporal distribution of events in a scenario. In this paper, we introduce an approach that enables the behaviour of people and the timing of events to be explicitly modelled through what we term dynamic factors. Our approach composes several simulation and modelling systems, including a wildfire simulator, behaviour modeller, and microscopic traffic simulator, to compute detailed projections of how scenarios unfold. The level of detail provided by our modelling approach enables the definition of a new risk metric, the exposure count, which directly quantifies the threat to a population. Experiments for a wildfire-prone region in Victoria, Australia, resulted in statistically significant differences in clearance times and exposure counts when comparing our modelling approach to an approach that does not account for dynamic factors. The approach has been implemented in a high performance and scalable system – the architecture of which is discussed – that allows multiple concurrent scenarios to be simulated in timeframes suitable for both planning and response use cases.} + title = {Simulation of wildfire evacuation with dynamic factors and model composition}, + journal = {Simulation Modelling Practice and Theory}, + volume = {60}, + pages = {144-159}, + year = {2016}, + issn = {1569-190X}, + doi = {10.1016/j.simpat.2015.10.002}, + url = {https://www.sciencedirect.com/science/article/pii/S1569190X15001483}, + author = {Anton Beloglazov and Mahathir Almashor and Ermyas Abebe and Jan Richter and Kent Charles Barton Steer}, + keywords = {Wildfire, Evacuation planning, Dynamic factors, Model composition, Behaviour and risk modelling}, + abstract = {Wildfires cause devastation on communities, most significantly loss of life. The safety of at-risk populations depends on accurate risk assessment and emergency planning. Evacuation modelling and simulation systems are essential tools for such planning and decision making. During a wildfire evacuation, the behaviour of people is a key factor; what people do, and when they do it, depends heavily on the spatio-temporal distribution of events in a scenario. In this paper, we introduce an approach that enables the behaviour of people and the timing of events to be explicitly modelled through what we term dynamic factors. Our approach composes several simulation and modelling systems, including a wildfire simulator, behaviour modeller, and microscopic traffic simulator, to compute detailed projections of how scenarios unfold. The level of detail provided by our modelling approach enables the definition of a new risk metric, the exposure count, which directly quantifies the threat to a population. Experiments for a wildfire-prone region in Victoria, Australia, resulted in statistically significant differences in clearance times and exposure counts when comparing our modelling approach to an approach that does not account for dynamic factors. The approach has been implemented in a high performance and scalable system – the architecture of which is discussed – that allows multiple concurrent scenarios to be simulated in timeframes suitable for both planning and response use cases.} } @article{doi:10.1061/JTEPBS.0000221, -author = {Paolo Intini and Enrico Ronchi and Steven Gwynne and Adam Pel }, -title = {Traffic Modeling for Wildland–Urban Interface Fire Evacuation}, -journal = {Journal of Transportation Engineering, Part A: Systems}, -volume = {145}, -number = {3}, -pages = {04019002}, -year = {2019}, -doi = {10.1061/JTEPBS.0000221}, - -URL = {https://ascelibrary.org/doi/abs/10.1061/JTEPBS.0000221}, -eprint = {https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.0000221} -, - abstract = { Several traffic modeling tools are currently available for evacuation planning and real-time decision support during emergencies. This paper reviews potential traffic-modeling approaches in the context of wildland–urban interface (WUI) fire-evacuation applications. Existing modeling approaches and features are evaluated pertaining to fire-related, spatial, and demographic factors; intended application (planning or decision support); and temporal issues. This systematic review shows the importance of the following modeling approaches: dynamic modeling structures, considering behavioral variability and route choice; activity-based models for short-notice evacuation planning; and macroscopic traffic simulation for real-time evacuation management. Subsequently, the modeling features of 22 traffic models and applications currently available in practice and the literature are reviewed and matched with the benchmark features identified for WUI fire applications. Based on this review analysis, recommendations are made for developing traffic models specifically applicable to WUI fire evacuation, including possible integrations with wildfire and pedestrian models. } + author = {Paolo Intini and Enrico Ronchi and Steven Gwynne and Adam Pel }, + title = {Traffic Modeling for Wildland–Urban Interface Fire Evacuation}, + journal = {Journal of Transportation Engineering, Part A: Systems}, + volume = {145}, + number = {3}, + pages = {04019002}, + year = {2019}, + doi = {10.1061/JTEPBS.0000221}, + url = {https://ascelibrary.org/doi/abs/10.1061/JTEPBS.0000221}, + eprint = {https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.0000221}, + abstract = { Several traffic modeling tools are currently available for evacuation planning and real-time decision support during emergencies. This paper reviews potential traffic-modeling approaches in the context of wildland–urban interface (WUI) fire-evacuation applications. Existing modeling approaches and features are evaluated pertaining to fire-related, spatial, and demographic factors; intended application (planning or decision support); and temporal issues. This systematic review shows the importance of the following modeling approaches: dynamic modeling structures, considering behavioral variability and route choice; activity-based models for short-notice evacuation planning; and macroscopic traffic simulation for real-time evacuation management. Subsequently, the modeling features of 22 traffic models and applications currently available in practice and the literature are reviewed and matched with the benchmark features identified for WUI fire applications. Based on this review analysis, recommendations are made for developing traffic models specifically applicable to WUI fire evacuation, including possible integrations with wildfire and pedestrian models. } } -@Article{Pel, - author={Adam Pel and Michiel Bliemer and Serge Hoogendoorn}, - title={{A review on travel behaviour modelling in dynamic traffic simulation models for evacuations}}, - journal={Transportation}, - year=2012, - volume={39}, - number={1}, - pages={97-123}, - month={January}, - keywords={Evacuation; Travel behaviour; Departure time choice; Destination choice; Route choice; Dynamic traff}, - doi={10.1007/s11116-011-9320-6}, - abstract={No abstract is available for this item.}, - url={https://ideas.repec.org/a/kap/transp/v39y2012i1p97-123.html} +@article{Pel, + author = {Adam Pel and Michiel Bliemer and Serge Hoogendoorn}, + title = {{A review on travel behaviour modelling in dynamic traffic simulation models for evacuations}}, + journal = {Transportation}, + year = 2012, + volume = {39}, + number = {1}, + pages = {97-123}, + month = {January}, + keywords = {Evacuation; Travel behaviour; Departure time choice; Destination choice; Route choice; Dynamic traff}, + doi = {10.1007/s11116-011-9320-6}, + abstract = {No abstract is available for this item.}, + url = {https://ideas.repec.org/a/kap/transp/v39y2012i1p97-123.html} } -@article{McCaffrey_2017, -title={Should I Stay or Should I Go Now? Or Should I Wait and See? Influences on Wildfire Evacuation Decisions}, -volume={38}, ISSN={1539-6924}, -url={http://dx.doi.org/10.1111/risa.12944}, -DOI={10.1111/risa.12944}, -number={7}, -journal={Risk Analysis}, -publisher={Wiley}, -author={McCaffrey, Sarah and Wilson, Robyn and Konar, Avishek}, -year={2017}, month=nov, pages={1390–1404} } +@article{McCaffrey_2017, + title = {Should I Stay or Should I Go Now? Or Should I Wait and See? Influences on Wildfire Evacuation Decisions}, + volume = {38}, + issn = {1539-6924}, + url = {http://dx.doi.org/10.1111/risa.12944}, + doi = {10.1111/risa.12944}, + number = {7}, + journal = {Risk Analysis}, + publisher = {Wiley}, + author = {McCaffrey, Sarah and Wilson, Robyn and Konar, Avishek}, + year = {2017}, + month = nov, + pages = {1390–1404} +} @book{rothermel1972mathematical, - title={A mathematical model for predicting fire spread in wildland fuels}, - author={Rothermel, Richard C}, - volume={115}, - year={1972}, - publisher={Intermountain Forest \& Range Experiment Station, Forest Service, US Department of Agriculture} + title = {A mathematical model for predicting fire spread in wildland fuels}, + author = {Rothermel, Richard C}, + volume = {115}, + year = {1972}, + publisher = {Intermountain Forest \& Range Experiment Station, Forest Service, US Department of Agriculture} } @book{Andrews_1986, - address={Ogden, UT}, - title={BEHAVE: fire behavior prediction and fuel modeling system-BURN Subsystem, part 1}, - url={https://www.fs.usda.gov/treesearch/pubs/29612}, - DOI={10.2737/INT-GTR-194}, - number={INT-GTR-194}, - institution={U.S. Department of Agriculture, Forest Service, Intermountain Research Station}, - author={Andrews, Patricia L.}, - year={1986}, - pages={INT-GTR-194}, - language={en} + address = {Ogden, UT}, + title = {BEHAVE: fire behavior prediction and fuel modeling system-BURN Subsystem, part 1}, + url = {https://www.fs.usda.gov/treesearch/pubs/29612}, + doi = {10.2737/INT-GTR-194}, + number = {INT-GTR-194}, + institution = {U.S. Department of Agriculture, Forest Service, Intermountain Research Station}, + author = {Andrews, Patricia L.}, + year = {1986}, + pages = {INT-GTR-194}, + language = {en} } @article{https://doi.org/10.1002/eap.1898, - author = {Joseph, Maxwell B. and Rossi, Matthew W. and Mietkiewicz, Nathan P. and Mahood, Adam L. and Cattau, Megan E. and St. Denis, Lise Ann and Nagy, R. Chelsea and Iglesias, Virginia and Abatzoglou, John T. and Balch, Jennifer K.}, - title = {Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima}, - journal = {Ecological Applications}, - volume = {29}, - number = {6}, - pages = {e01898}, - keywords = {Bayesian, climate, extremes, fire, spatiotemporal, wildfire}, - doi = {10.1002/eap.1898}, - url = {https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/eap.1898}, - eprint = {https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/eap.1898}, - abstract = {Abstract Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we integrate a 30-yr wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). A zero-inflated negative binomial model for fire counts and a lognormal model for burned areas provided the best performance. This model attains 99\% interval coverage for the number of fires and 93\% coverage for fire sizes over a six year withheld data set. Dryness and air temperature strongly predict extreme wildfire probabilities. Housing density has a hump-shaped relationship with fire occurrence, with more fires occurring at intermediate housing densities. Statistically, these drivers affect the chance of an extreme wildfire in two ways: by altering fire size distributions, and by altering fire frequency, which influences sampling from the tails of fire size distributions. We conclude that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.}, - year = {2019} + author = {Joseph, Maxwell B. and Rossi, Matthew W. and Mietkiewicz, Nathan P. and Mahood, Adam L. and Cattau, Megan E. and St. Denis, Lise Ann and Nagy, R. Chelsea and Iglesias, Virginia and Abatzoglou, John T. and Balch, Jennifer K.}, + title = {Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima}, + journal = {Ecological Applications}, + volume = {29}, + number = {6}, + pages = {e01898}, + keywords = {Bayesian, climate, extremes, fire, spatiotemporal, wildfire}, + doi = {10.1002/eap.1898}, + url = {https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/eap.1898}, + eprint = {https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/eap.1898}, + abstract = {Abstract Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we integrate a 30-yr wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). A zero-inflated negative binomial model for fire counts and a lognormal model for burned areas provided the best performance. This model attains 99\% interval coverage for the number of fires and 93\% coverage for fire sizes over a six year withheld data set. Dryness and air temperature strongly predict extreme wildfire probabilities. Housing density has a hump-shaped relationship with fire occurrence, with more fires occurring at intermediate housing densities. Statistically, these drivers affect the chance of an extreme wildfire in two ways: by altering fire size distributions, and by altering fire frequency, which influences sampling from the tails of fire size distributions. We conclude that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.}, + year = {2019} } -@inproceedings{rempel_shiell_2022, -title={Using Reinforcement Learning to Provide Decision Support in Multi-Domain Mass Evacuation Operations}, -url={https://review.sto.nato.int/index.php/journal-issues/2023-fall/sas-ora-conference-2022/68-using-reinforcement-learning-to-provide-decision-support-in-multi-domain-mass-evacuation-operations}, - booktitle={NATO Operations Research and Analysis Conference}, - author={Rempel, Mark and Shiell, Nicholi}, - year={2022}, - month={October}, - address = {Copenhagen, Denmark}} +@inproceedings{rempel_shiell_2022, + title = {Using Reinforcement Learning to Provide Decision Support in Multi-Domain Mass Evacuation Operations}, + url = {https://review.sto.nato.int/index.php/journal-issues/2023-fall/sas-ora-conference-2022/68-using-reinforcement-learning-to-provide-decision-support-in-multi-domain-mass-evacuation-operations}, + booktitle = {NATO Operations Research and Analysis Conference}, + author = {Rempel, Mark and Shiell, Nicholi}, + year = {2022}, + month = {October}, + address = {Copenhagen, Denmark} +} @article{10.1063/5.0209018, - author = {Budakova, Dilyana and Vasilev, Velyo and Dakovski, Lyudmil}, - title = "{A reinforcement learning algorithm for the optimal evacuation route finding from an electrical substation}", - journal = {AIP Conference Proceedings}, - volume = {3078}, - number = {1}, - pages = {040005}, - year = {2024}, - month = {04}, - abstract = "{In this paper, the Intensity of the Characteristic Q-learning (InCh Q-learning) algorithm is proposed. It allows for finding the shortest and at the same time the safest escape route. Data on the intensity and spread of a fire occurring in a virtual electrical substation are used. Matrices are entered with the intensity values of each considered fire characteristic. As the fire spreads in space, zones of intensity are formed. Rules are introduced that take into account intensity zones for finding an escape route. The learning algorithm finds the shortest path for which the intensity of each of the dangerous features is the least. A threshold of the intensity of each character is introduced at which a person can pass and evacuate successfully. The use of priorities for the dangerous features allows one to choose a path with a greater intensity of the safer features and at the same time with a lower intensity of the more dangerous ones. The optimal escape route found is used to build a decision tree to find the location of an injured user as quickly as possible.}", - issn = {0094-243X}, - doi = {10.1063/5.0209018}, - url = {https://doi.org/10.1063/5.0209018}, - eprint = {https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0209018/19898413/040005\_1\_5.0209018.pdf}, + author = {Budakova, Dilyana and Vasilev, Velyo and Dakovski, Lyudmil}, + title = {{A reinforcement learning algorithm for the optimal evacuation route finding from an electrical substation}}, + journal = {AIP Conference Proceedings}, + volume = {3078}, + number = {1}, + pages = {040005}, + year = {2024}, + month = {04}, + abstract = {{In this paper, the Intensity of the Characteristic Q-learning (InCh Q-learning) algorithm is proposed. It allows for finding the shortest and at the same time the safest escape route. Data on the intensity and spread of a fire occurring in a virtual electrical substation are used. Matrices are entered with the intensity values of each considered fire characteristic. As the fire spreads in space, zones of intensity are formed. Rules are introduced that take into account intensity zones for finding an escape route. The learning algorithm finds the shortest path for which the intensity of each of the dangerous features is the least. A threshold of the intensity of each character is introduced at which a person can pass and evacuate successfully. The use of priorities for the dangerous features allows one to choose a path with a greater intensity of the safer features and at the same time with a lower intensity of the more dangerous ones. The optimal escape route found is used to build a decision tree to find the location of an injured user as quickly as possible.}}, + issn = {0094-243X}, + doi = {10.1063/5.0209018}, + url = {https://doi.org/10.1063/5.0209018}, + eprint = {https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0209018/19898413/040005\_1\_5.0209018.pdf} } @inproceedings{satelliteimages2017, -author = {Subramanian, Sriram and Crowley, Mark}, -year = {2017}, -month = {01}, -pages = {}, -booktitle = {The 3rd Multi-disciplinary Conference on Reinforcement Learning and Decision Making}, -title = {Learning Forest Wildfire Dynamics from Satellite Images Using Reinforcement Learning} + author = {Subramanian, Sriram and Crowley, Mark}, + year = {2017}, + month = {01}, + pages = {}, + booktitle = {The 3rd Multi-disciplinary Conference on Reinforcement Learning and Decision Making}, + title = {Learning Forest Wildfire Dynamics from Satellite Images Using Reinforcement Learning} } -@InProceedings{Diao2020, -author = {Tina Diao and Samriddhi Singla and Ayan Mukhopadhyay and Ahmed Eldawy and Ross Shachter and Mykel J. Kochenderfer}, -booktitle = {AIAA Fall Symposium}, -title = {Uncertainty aware wildfire management}, -year = {2020}, -url = {https://arxiv.org/pdf/2010.07915.pdf}, -doi = {10.48550/arXiv.2010.07915} +@inproceedings{Diao2020, + author = {Tina Diao and Samriddhi Singla and Ayan Mukhopadhyay and Ahmed Eldawy and Ross Shachter and Mykel J. Kochenderfer}, + booktitle = {AIAA Fall Symposium}, + title = {Uncertainty aware wildfire management}, + year = {2020}, + url = {https://arxiv.org/pdf/2010.07915.pdf}, + doi = {10.48550/arXiv.2010.07915} } @inproceedings{ross2021being, - title={Being the Fire: A CNN-Based Reinforcement Learning Method to Learn How Fires Behave Beyond the Limits of Physics-Based Empirical Models}, - author={Ross, William L}, - booktitle={NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning}, - url={https://www.climatechange.ai/papers/neurips2021/22}, - year={2021} + title = {Being the Fire: A CNN-Based Reinforcement Learning Method to Learn How Fires Behave Beyond the Limits of Physics-Based Empirical Models}, + author = {Ross, William L}, + booktitle = {NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning}, + url = {https://www.climatechange.ai/papers/neurips2021/22}, + year = {2021} } @article{ganapathi2018using, - title={{Using spatial reinforcement learning to build forest wildfire dynamics models from satellite images}}, - author={Ganapathi Subramanian, Sriram and Crowley, Mark}, - journal={{Frontiers in Information and Communication Technology}}, - volume={5}, - pages={6}, - year={2018}, - publisher={Frontiers Media SA}, - doi = {10.3389/FICT.2018.00006} + title = {{Using spatial reinforcement learning to build forest wildfire dynamics models from satellite images}}, + author = {Ganapathi Subramanian, Sriram and Crowley, Mark}, + journal = {{Frontiers in Information and Communication Technology}}, + volume = {5}, + pages = {6}, + year = {2018}, + publisher = {Frontiers Media SA}, + doi = {10.3389/FICT.2018.00006} } -@Article{Julian2019jgcdfire, -author = {Kyle D. Julian and Mykel J. Kochenderfer}, -journal = jgcd, -title = {Distributed wildfire surveillance with autonomous aircraft using deep reinforcement learning}, -year = {2019}, -number = {8}, -pages = {1768--1778}, -volume = {42}, -doi = {10.2514/1.G004106}, -url = {https://arxiv.org/abs/1810.04244}, +@article{Julian2019jgcdfire, + author = {Kyle D. Julian and Mykel J. Kochenderfer}, + journal = jgcd, + title = {Distributed wildfire surveillance with autonomous aircraft using deep reinforcement learning}, + year = {2019}, + number = {8}, + pages = {1768--1778}, + volume = {42}, + doi = {10.2514/1.G004106}, + url = {https://arxiv.org/abs/1810.04244} } @article{altamimi2022large, - title={{Large-Scale Wildfire Mitigation Through Deep Reinforcement Learning}}, - author={Altamimi, Abdulelah and Lagoa, Constantino and Borges, Jos{\'e} G and McDill, Marc E and Andriotis, CP and Papakonstantinou, KG}, - journal={Frontiers in Forests and Global Change}, - volume={5}, - pages={734330}, - year={2022}, - publisher={Frontiers Media SA}, - doi = {10.3389/ffgc.2022.734330} + title = {{Large-Scale Wildfire Mitigation Through Deep Reinforcement Learning}}, + author = {Altamimi, Abdulelah and Lagoa, Constantino and Borges, Jos{\'e} G and McDill, Marc E and Andriotis, CP and Papakonstantinou, KG}, + journal = {Frontiers in Forests and Global Change}, + volume = {5}, + pages = {734330}, + year = {2022}, + publisher = {Frontiers Media SA}, + doi = {10.3389/ffgc.2022.734330} } -@ARTICLE{9340340, - author={Viseras, Alberto and Meissner, Michael and Marchal, Juan}, - journal={IEEE Access}, - title={{Wildfire Front Monitoring with Multiple UAVs using Deep Q-Learning}}, - year={2021}, - volume={}, - number={}, - pages={1-1}, - doi={10.1109/ACCESS.2021.3055651} +@article{9340340, + author = {Viseras, Alberto and Meissner, Michael and Marchal, Juan}, + journal = {IEEE Access}, + title = {{Wildfire Front Monitoring with Multiple UAVs using Deep Q-Learning}}, + year = {2021}, + volume = {}, + number = {}, + pages = {1-1}, + doi = {10.1109/ACCESS.2021.3055651} } @article{https://doi.org/10.1111/risa.12944, - author = {McCaffrey, Sarah and Wilson, Robyn and Konar, Avishek}, - title = {{Should I Stay or Should I Go Now? Or Should I Wait and See? Influences on Wildfire Evacuation Decisions}}, - journal = {Risk Analysis}, - volume = {38}, - number = {7}, - pages = {1390-1404}, - keywords = {Decision making, evacuation, risk attitudes, wildfires}, - doi = {10.1111/risa.12944}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/risa.12944}, - eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/risa.12944}, - abstract = {Abstract As climate change has contributed to longer fire seasons and populations living in fire-prone ecosystems increase, wildfires have begun to affect a growing number of people. As a result, interest in understanding the wildfire evacuation decision process has increased. Of particular interest is understanding why some people leave early, some choose to stay and defend their homes, and others wait to assess conditions before making a final decision. Individuals who tend to wait and see are of particular concern given the dangers of late evacuation. To understand what factors might influence different decisions, we surveyed homeowners in three areas in the United States that recently experienced a wildfire. The Protective Action Decision Model was used to identify a suite of factors previously identified as potentially relevant to evacuation decisions. Our results indicate that different beliefs about the efficacy of a particular response or action (evacuating or staying to defend), differences in risk attitudes, and emphasis on different cues to act (e.g., official warnings, environmental cues) are key factors underlying different responses. Further, latent class analysis indicates there are two general classes of individuals: those inclined to evacuate and those inclined to stay, and that a substantial portion of each class falls into the wait and see category.}, - year = {2018} + author = {McCaffrey, Sarah and Wilson, Robyn and Konar, Avishek}, + title = {{Should I Stay or Should I Go Now? Or Should I Wait and See? Influences on Wildfire Evacuation Decisions}}, + journal = {Risk Analysis}, + volume = {38}, + number = {7}, + pages = {1390-1404}, + keywords = {Decision making, evacuation, risk attitudes, wildfires}, + doi = {10.1111/risa.12944}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/risa.12944}, + eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/risa.12944}, + abstract = {Abstract As climate change has contributed to longer fire seasons and populations living in fire-prone ecosystems increase, wildfires have begun to affect a growing number of people. As a result, interest in understanding the wildfire evacuation decision process has increased. Of particular interest is understanding why some people leave early, some choose to stay and defend their homes, and others wait to assess conditions before making a final decision. Individuals who tend to wait and see are of particular concern given the dangers of late evacuation. To understand what factors might influence different decisions, we surveyed homeowners in three areas in the United States that recently experienced a wildfire. The Protective Action Decision Model was used to identify a suite of factors previously identified as potentially relevant to evacuation decisions. Our results indicate that different beliefs about the efficacy of a particular response or action (evacuating or staying to defend), differences in risk attitudes, and emphasis on different cues to act (e.g., official warnings, environmental cues) are key factors underlying different responses. Further, latent class analysis indicates there are two general classes of individuals: those inclined to evacuate and those inclined to stay, and that a substantial portion of each class falls into the wait and see category.}, + year = {2018} } @software{cellular_automata, title = {{Gym Cellular Automata}}, - year={2021}, - url = {https://github.com/elbecerrasoto/gym-cellular-automata}, + year = {2021}, + url = {https://github.com/elbecerrasoto/gym-cellular-automata} } @software{forest_fire, title = {{Gym Forest Fire}}, - year={2020}, - url = {https://github.com/sahandrez/gym_forestfire}, + year = {2020}, + url = {https://github.com/sahandrez/gym_forestfire} } @article{Julian2019, - title = {Distributed Wildfire Surveillance with Autonomous Aircraft Using Deep Reinforcement Learning}, - volume = {42}, - ISSN = {1533-3884}, - url = {http://dx.doi.org/10.2514/1.G004106}, - DOI = {10.2514/1.g004106}, - number = {8}, - journal = {Journal of Guidance, Control, and Dynamics}, + title = {Distributed Wildfire Surveillance with Autonomous Aircraft Using Deep Reinforcement Learning}, + volume = {42}, + issn = {1533-3884}, + url = {http://dx.doi.org/10.2514/1.G004106}, + doi = {10.2514/1.g004106}, + number = {8}, + journal = {Journal of Guidance, Control, and Dynamics}, publisher = {American Institute of Aeronautics and Astronautics (AIAA)}, - author = {Julian, Kyle D. and Kochenderfer, Mykel J.}, - year = {2019}, - month = aug, - pages = {1768–1778} + author = {Julian, Kyle D. and Kochenderfer, Mykel J.}, + year = {2019}, + month = aug, + pages = {1768–1778} } @book{kochenderfer2022algorithms, - title={Algorithms for decision making}, - author={Kochenderfer, Mykel J and Wheeler, Tim A and Wray, Kyle H}, - year={2022}, - publisher={MIT press}, - isbn={0262047012} + title = {Algorithms for decision making}, + author = {Kochenderfer, Mykel J and Wheeler, Tim A and Wray, Kyle H}, + year = {2022}, + publisher = {MIT press}, + isbn = {0262047012} } @article{stableBaselines,