4.5 Article

Large-Scale Wildfire Mitigation Through Deep Reinforcement Learning

Related references

Note: Only part of the references are listed.
Article Engineering, Industrial

Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints

C. P. Andriotis et al.

Summary: Determining inspection and maintenance policies to minimize long-term risks and costs in deteriorating engineering environments is a complex optimization problem. Major computational challenges include the curse of dimensionality, curse of history, presence of state uncertainties, and presence of constraints. These challenges are addressed through a joint framework of constrained Partially Observable Markov Decision Processes (POMDP) and multi-agent Deep Reinforcement Learning (DRL).

RELIABILITY ENGINEERING & SYSTEM SAFETY (2021)

Article Environmental Sciences

Wildfire management in Mediterranean-type regions: paradigm change needed

Francisco Moreira et al.

ENVIRONMENTAL RESEARCH LETTERS (2020)

Article Environmental Sciences

Climate change is increasing the likelihood of extreme autumn wildfire conditions across California

Michael Goss et al.

ENVIRONMENTAL RESEARCH LETTERS (2020)

Article Engineering, Industrial

Managing engineering systems with large state and action spaces through deep reinforcement learning

C. P. Andriotis et al.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2019)

Article Environmental Sciences

Observed Impacts of Anthropogenic Climate Change on Wildfire in California

A. Park Williams et al.

EARTHS FUTURE (2019)

Article Green & Sustainable Science & Technology

Modeling Post-Fire Mortality in Pure and Mixed Forest Stands in Portugal-A Forest Planning-Oriented Model

Brigite Botequim et al.

SUSTAINABILITY (2017)

Article Green & Sustainable Science & Technology

Addressing Wildfire Risk in Forest Management Planning with Multiple Criteria Decision Making Methods

Susete Marques et al.

SUSTAINABILITY (2017)

Article Multidisciplinary Sciences

Human-level control through deep reinforcement learning

Volodymyr Mnih et al.

NATURE (2015)

Article Engineering, Industrial

Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part I: Theory

K. G. Papakonstantinou et al.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2014)

Article Computer Science, Interdisciplinary Applications

Are the applications of wildland fire behaviour models getting ahead of their evaluation again?

Martin E. Alexander et al.

ENVIRONMENTAL MODELLING & SOFTWARE (2013)

Article Forestry

Integrating fire risk considerations in landscape-level forest planning

Jose-Ramon Gonzalez-Olabarria et al.

FOREST ECOLOGY AND MANAGEMENT (2011)

Article Computer Science, Artificial Intelligence

Real-time reinforcement learning by sequential Actor-Critics and experience replay

Pawel Wawrzynski

NEURAL NETWORKS (2009)

Article Forestry

A fire probability model for forest stands in Catalonia (north-east Spain)

JR González et al.

ANNALS OF FOREST SCIENCE (2006)