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Data assimilation using sequential monte carlo methods in wildfire spread simulation

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Sequential Monte Carlo Methods in Practice

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Particle Filters for Mobile Robot Localization

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Particle Filters — A Theoretical Perspective

inferredcites →https://doi.org/10.1007/978-3-540-24688-6_94openalex-snapshot

A Note on Dynamic Data Driven Wildfire Modeling

inferredcites →https://doi.org/10.1007/s10236-003-0036-9openalex-snapshot

The Ensemble Kalman Filter: theoretical formulation and practical implementation

inferredcites →https://doi.org/10.1016/0360-1285(91)90003-6openalex-snapshot

Modelling fire spread through fuel beds

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Theory of Modeling and Simulation

inferredcites →https://doi.org/10.1016/j.compchemeng.2007.03.012openalex-snapshot

Dynamic data rectification using particle filters

inferredcites →https://doi.org/10.1016/j.matcom.2008.03.015openalex-snapshot

A wildland fire model with data assimilation

inferredcites →https://doi.org/10.1016/j.rse.2007.05.005openalex-snapshot

Mapping surface fuel models using lidar and multispectral data fusion for fire behavior

inferredcites →https://doi.org/10.1016/s0076-5392(09)x6022-4openalex-snapshot

Stochastic Processes and Filtering Theory

inferredcites →https://doi.org/10.1016/s0360-1285(03)00017-0openalex-snapshot

Mathematical models and calculation systems for the study of wildland fire behaviour

inferredcites →https://doi.org/10.1017/cbo9780511802270openalex-snapshot

Atmospheric Modeling, Data Assimilation and Predictability

inferredcites →https://doi.org/10.1049/ip-f-2.1993.0015openalex-snapshot

Novel approach to nonlinear/non-Gaussian Bayesian state estimation

inferredcites →https://doi.org/10.1063/1.1554395openalex-snapshot

Origin of scaling behavior of protein packing density: A sequential Monte Carlo study of compact long chain polymers

inferredcites →https://doi.org/10.1071/wf02007openalex-snapshot

Studying wildfire behavior using FIRETEC

inferredcites →https://doi.org/10.1071/wf02055openalex-snapshot

Autonomous field-deployable wildland fire sensors

inferredcites →https://doi.org/10.1109/78.978396openalex-snapshot

Particle filters for positioning, navigation, and tracking

inferredcites →https://doi.org/10.1109/igarss.2008.4779375openalex-snapshot

Four-Dimensional Variational Assimilation of Satellite Temperature and Sea Level Data in the Coastal Ocean and Adjacent Deep Sea

inferredcites →https://doi.org/10.1109/itsc.2006.1706847openalex-snapshot

Non-linear kalman filtering algorithms for on-line calibration of dynamic traffic assignment models

inferredcites →https://doi.org/10.1109/mcs.2009.932224openalex-snapshot

Data assimilation for wildland fires

inferredcites →https://doi.org/10.1109/tits.2007.908569openalex-snapshot

Nonlinear Kalman Filtering Algorithms for On-Line Calibration of Dynamic Traffic Assignment Models

inferredcites →https://doi.org/10.1109/twc.2007.05912openalex-snapshot

Mobility Tracking in Cellular Networks Using Particle Filtering

inferredcites →https://doi.org/10.1109/wsc.2004.1371428openalex-snapshot

Proceedings of the 2004 Winter Simulation Conference

inferredcites →https://doi.org/10.1109/wsc.2006.323011openalex-snapshot

DDDAS Approaches to Wildland Fire Modeling and Contaminant Tracking

inferredcites →https://doi.org/10.1109/wsc.2009.5429281openalex-snapshot

A dynamic data driven application system for wildfire spread simulation

inferredcites →https://doi.org/10.1126/science.1159607openalex-snapshot

Ecosystem Disturbance, Carbon, and Climate

inferredcites →https://doi.org/10.1139/x73-055openalex-snapshot

Height of Crown Scorch in Forest Fires

inferredcites →https://doi.org/10.1142/9789812700919_0004openalex-snapshot

SEQUENTIAL MONTE CARLO METHODS AND THEIR APPLICATIONS

inferredcites →https://doi.org/10.1145/1596519.1596524openalex-snapshot

Integrated simulation and optimization for wildfire containment

inferredcites →https://doi.org/10.1145/2043635.2043641openalex-snapshot

A New Algorithm for Simulating Wildfire Spread through Cellular Automata

inferredcites →https://doi.org/10.1177/0037549708094047openalex-snapshot

DEVS-FIRE: Towards an Integrated Simulation Environment for Surface Wildfire Spread and Containment

inferredcites →https://doi.org/10.1177/0037549711414592openalex-snapshot

DEVS-FIRE: design and application of formal discrete event wildfire spread and suppression models

inferredcites →https://doi.org/10.1198/tech.2003.s23openalex-snapshot

Sequential Monte Carlo Methods in Practice

inferredcites →https://doi.org/10.1198/tech.2005.s326openalex-snapshot

Atmospheric Modeling, Data Assimilation, and Predictability

inferredcites →https://doi.org/10.2514/6.2007-6757openalex-snapshot

Particle Filter Based Mosaicking for Tracking Forest Fires

inferredcites →https://doi.org/10.2737/int-rp-115openalex-snapshot

A mathematical model for predicting fire spread in wildland fuels

inferredcites →https://doi.org/10.2737/rmrs-gtr-106openalex-snapshot

BehavePlus fire modeling system, version 4.0: User's Guide

inferredcites →https://doi.org/10.2737/rmrs-rp-4openalex-snapshot

FARSITE: Fire Area Simulator-model development and evaluation

inferredcites →https://doi.org/10.3217/jucs-012-06-0608openalex-snapshot

Sequential Data Assimilation: Information Fusion of a Numerical Simulation and Large Scale Observation Data

inferredcites →https://doi.org/10.4267/2042/51948openalex-snapshot

Atmospheric data analysis

inferredcites →https://doi.org/10.48550/arxiv.0712.3965openalex-snapshot

Data Assimilation for Wildland Fires: Ensemble Kalman filters in coupled atmosphere-surface models

inferredcites →https://doi.org/10.5194/acp-8-7353-2008openalex-snapshot

Using 3DVAR data assimilation system to improve ozone simulations in the Mexico City basin

inferredcites →https://doi.org/10.5281/zenodo.15969275openalex-snapshot

FUTURE OF CALIFORNIA FLORISTICS AND SYSTEMATICS: WILDFIRE THREATS TO THE CALIFORNIA FLORA

inferredcites →https://doi.org/10.5860/choice.29-3907openalex-snapshot

Atmospheric data analysis

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Wildland Fire Spread Modeling Using Convolutional Neural Networks

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Coupled fire–atmosphere modeling of wildland fire spread using DEVS-FIRE and ARPS

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Data assimilation method for quantifying controlled methane releases using a drone and ground-sensors

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Physics-informed neural networks for parameter learning of wildfire spreading

inferred← citeshttps://doi.org/10.1016/j.ecolmodel.2016.12.022openalex-snapshot

Forest fire spread simulating model using cellular automaton with extreme learning machine

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Driving wildfire spread prediction by multi-source real-time observations

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Development of novel optimized deep learning algorithms for wildfire modeling: A case study of Maui, Hawai‘i

inferred← citeshttps://doi.org/10.1016/j.firesaf.2019.02.009openalex-snapshot

Ensemble transform Kalman filter (ETKF) for large-scale wildland fire spread simulation using FARSITE tool and state estimation method

inferred← citeshttps://doi.org/10.1016/j.jocs.2015.08.009openalex-snapshot

GPU-accelerated multi-objective optimization of fuel treatments for mitigating wildfire hazard

inferred← citeshttps://doi.org/10.1016/j.jocs.2016.08.003openalex-snapshot

Parallel fuzzy cellular automata for data-driven simulation of wildfire spreading

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A novel hybrid resampling algorithm for parallel/distributed particle filters

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A multi-objective discrete particle swarm optimization method for particle routing in distributed particle filters

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Machine learning for wildfire classification: Exploring blackbox, eXplainable, symbolic, and SMOTE methods

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Combustion machine learning: Principles, progress and prospects

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Building occupancy simulation and data assimilation using a graph-based agent-oriented model

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Wildfire Spread Prediction and Assimilation for FARSITE Using Ensemble Kalman Filtering 1

inferred← citeshttps://doi.org/10.1016/j.procs.2017.05.197openalex-snapshot

Data Assimilation of Wildfires with Fuel Adjustment Factors in farsite using Ensemble Kalman Filtering * *This work is funded by NSF 1331615 under CI, Information Technology Research and SEES Hazards programs

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Assessing Pan-Canada wildfire susceptibility by integrating satellite data with novel hybrid deep learning and black widow optimizer algorithms

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On-demand data assimilation of large-scale spatial temporal systems using sequential Monte Carlo methods

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Bayesian Physics Informed Neural Networks for data assimilation and spatio-temporal modelling of wildfires

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A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

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Activity-informed Dynamic Data Driven Simulation

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Prescribed Fire Simulation with Dynamic Ignitions Using Data from UAS-based Sensing

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Random finite set based data assimilation algorithm for dynamic data driven simulation

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Real-time data driven simulation of air contaminant dispersion using particle filter and UAV sensory system

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Distributed Dynamic Data Driven Simulations: Basic Idea and an Illustration Example

inferred← citeshttps://doi.org/10.1109/icuas.2019.8797793openalex-snapshot

Wildfire Monitoring with Uneven Importance Using Multiple Unmanned Aircraft Systems

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Recent Trends in Modelling and Simulation with Machine Learning

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Towards A Map-Based Web Application for Prescribed Fire Simulation

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Kalman Filter-Based Large-Scale Wildfire Monitoring With a System of UAVs

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Path Planning for First Responders in the Presence of Moving Obstacles With Uncertain Boundaries

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Particle Routing in Distributed Particle Filters for Large-Scale Spatial Temporal Systems

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Behavior Pattern Detection for Data Assimilation in Agent-Based Simulation of Smart Environments

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An effective proposal distribution for sequential Monte Carlo methods-based wildfire data assimilation

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