Data-driven modeling of wildfire spread with stochastic cellular automata and latent spatio-temporal dynamics

Grieshop, Nicholas; Wikle, Christopher K.

doi:10.1016/j.spasta.2023.100794

Spatial Statistics

2024

DOI: 10.1016/j.spasta.2023.100794

|View full text |Cite

Data-driven modeling of wildfire spread with stochastic cellular automata and latent spatio-temporal dynamics

Nicholas Grieshop,

Christopher K. Wikle

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Modeling Anisotropy and Non‐Stationarity Through Physics‐Informed Spatial Regression

Tomasetto,

Arnone,

Sangalli

2024

Environmetrics

View full text Add to dashboard Cite

Many spatially dependent phenomena that are of interest in environmental problems are characterized by strong anisotropy and non‐stationarity. Moreover, the data are often observed over regions with complex conformations, such as water bodies with complicated shorelines or regions with complex orography. Furthermore, the distribution of the data locations may be strongly inhomogeneous over space. These issues may challenge popular approaches to spatial data analysis. In this work, we show how we can accurately address these issues by spatial regression with differential regularization. We model the spatial variation by a Partial Differential Equation (PDE), defined upon the considered spatial domain. This PDE may depend upon some unknown parameters that we estimate from the data through an appropriate profiling estimation approach. The PDE may encode some available problem‐specific information on the considered phenomenon, and permit a rich modeling of anisotropy and non‐stationarity. The performances of the proposed approach are compared to competing methods through simulation studies and real data applications. In particular, we analyze rainfall data over Switzerland, characterized by strong anisotropy, and oceanographic data in the Gulf of Mexico, characterized by non‐stationarity due to the Gulf Stream.

show abstract

Modeling Anisotropy and Non‐Stationarity Through Physics‐Informed Spatial Regression

Tomasetto,

Arnone,

Sangalli

2024

Environmetrics

View full text Add to dashboard Cite

show abstract

Echo state network-enhanced symbolic regression for spatio-temporal binary stochastic cellular automata

Grieshop,

Wikle

2024

Spatial Statistics

View full text Add to dashboard Cite

Complex Battery Storage Fire Propagation Translational Forensic Study Using Cellular Automata

Roghani,

Braxtan,

Chen

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The surge in lithium-ion battery (LIB) use, essential for mass-scale renewable energy storage, raises concerns about fire hazards. However, to date, there is a lack of industry-wide understanding of large-scale LIB fire propagation. This paper suggests a translational forensic approach to promote fire safety awareness and introduces the cellular automata (CA) model coupled with the Monte Carlo (MC) approach to address the complex fire propagation simulation within an energy storage system (ESS). The objective is to demonstrate that the CA-MC model can provide a flexible and scalable connection for all levels of battery fire studies. The numerical model is coupled with experimental tests which have been performed to establish the actual timing of fire propagation from a single source. Cellular automata simulation, conducted through hybrid modeling and an applied risk analysis approach to evaluate fire hazards associated with LIBs, offers crucial insights into potential risks. The results demonstrate that, with fire incident initiation at a probability of 0.1 (10%), 33% of batteries will burn, and at a probability of 0.6 (60%) and beyond, the entire battery module will face complete burndown. Achieving full combustion of the entire module will take only approximately 42 timesteps on average, indicating rapid fire propagation. The actual time for a complete fire to occur in the battery module has been estimated to be 304 s per timestep, or 3.5 h total. Using this example, it is shown that the CA-MC approach can be extended to many other aspects of battery fire studies and is ideal as a translational tool, spanning all domains of the LIB industry.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Data-driven modeling of wildfire spread with stochastic cellular automata and latent spatio-temporal dynamics

Cited by 3 publications

References 25 publications

Modeling Anisotropy and Non‐Stationarity Through Physics‐Informed Spatial Regression

Modeling Anisotropy and Non‐Stationarity Through Physics‐Informed Spatial Regression

Echo state network-enhanced symbolic regression for spatio-temporal binary stochastic cellular automata

Complex Battery Storage Fire Propagation Translational Forensic Study Using Cellular Automata

Contact Info

Product

Resources

About