PRA

NASA’s Probabilistic Risk Assessment (PRA), Risk-Informed Decision Making (RIDM), and Continuous Risk Management (CRM) form a comprehensive framework for managing risks in complex systems and missions. These approaches have evolved over decades, drawing from experiences in various high-risk industries and NASA’s own space exploration endeavors.

Probabilistic Risk Assessment (PRA) at NASA has its roots in the nuclear power industry and was adapted for space applications following the Challenger disaster in 1986. PRA is a systematic and comprehensive methodology to evaluate risks associated with complex engineered technological entities. It involves identifying potential failure scenarios, estimating their probabilities, and assessing their consequences. NASA has applied PRA to various missions, including the Space Shuttle and International Space Station, to quantify risks and inform decision-making processes.

Risk-Informed Decision Making (RIDM) is a deliberative process that uses a diverse set of performance measures, together with other considerations, to inform decision making. NASA developed RIDM to enhance its ability to make critical decisions about design, development, and operation of complex systems. RIDM integrates risk analysis into the decision-making process, ensuring that decisions are made with an awareness of the risks involved and the potential impacts on mission success and safety.

Continuous Risk Management (CRM) is an ongoing process that is used to continuously identify, analyze, plan, track, control, communicate, and document risks. NASA implements CRM throughout the lifecycle of its projects and programs to proactively manage risks. This approach allows for the identification of new risks as they emerge and the reassessment of known risks as conditions change or new information becomes available.

These risk management approaches are interconnected within NASA’s overall risk management framework. PRA provides the quantitative basis for understanding risks, RIDM uses this information to inform critical decisions, and CRM ensures that risk management is an ongoing process throughout the life of a project or mission. This integrated approach has been crucial in managing the complex risks associated with space exploration and has been adapted for use in other high-risk industries.

The evolution of these risk management approaches at NASA reflects a broader trend in safety-critical industries towards more systematic, quantitative, and proactive risk management. Similar approaches have been adopted in fields such as aviation, nuclear power, and chemical processing. NASA’s experience with these methodologies has not only improved its own risk management capabilities but has also contributed to the broader field of risk analysis and management in complex technological systems.

Reviews

Expert systems in wildland fire management have evolved significantly over the past few decades, incorporating various aspects of risk assessment, decision support, and resource management. These systems range from general forest management tools to specialized applications for fire prediction, health risk assessment, and economic optimization, reflecting a trend towards more integrated, multifaceted approaches in fire management[114].

The INFORMS-TX (Integrated Forest Resource Management System in Texas), developed in 1994 by the USDA Forest Service and Texas A&M University, represents a pioneering effort in integrating rule-based expert systems with Geographic Information Systems (GIS) through a relational database management system. This early integration laid the groundwork for more sophisticated forest resource management tools, potentially influencing modern systems such as InFORM[129].

As expert systems evolved, they began to address more specific regional contexts. For instance, an intelligent system for forest fire risk prediction and fire fighting management was developed for Galicia, Spain in 2003. This trend towards tailored solutions for different geographical areas underscores the importance of considering local factors in wildfire management, aligning with the principles of NASA’s Risk-Informed Decision Making (RIDM) framework, which emphasizes the need for context-specific risk assessments[132].

The BehavePlus Fire Modeling System, reviewed in 2013, exemplifies the ongoing advancement in fire behavior prediction tools. This review highlighted the necessity for continuous updates and improvements in modeling capabilities, reflecting the dynamic nature of wildfire management challenges[118]. This approach resonates with NASA’s Continuous Risk Management (CRM) philosophy, which advocates for ongoing risk assessment and mitigation strategies[151].

Expert systems have also been applied to specific ecosystem management practices. FIRETOOL, developed in 1996, focused on the use of prescribed fires in Brazilian savannas. This application demonstrates the adaptability of expert systems to diverse ecological contexts and management strategies, a crucial aspect in the comprehensive risk management approach advocated by NASA’s PRA framework[163].

Health considerations have become increasingly integrated into wildfire management expert systems. Studies on health risks for wildland firefighters due to chronic smoke exposure and firefighters’ exposure risks to bushfire smoke highlight the incorporation of occupational health considerations into fire management practices. This broadening scope aligns with the holistic risk assessment approach of NASA’s PRA framework, which considers multiple facets of risk in complex systems[169].

Economic aspects of wildfire management have also been addressed through expert systems. A forest management risk insurance model developed in 2006 for coniferous stands in southwest Germany introduced economic risk management tools to the forestry sector. Similarly, a 2010 study on the economic optimization of wildfire intervention activities represents a significant step towards integrating cost-effectiveness into fire management decision-making processes. These economic considerations parallel NASA’s RIDM approach, which emphasizes the importance of considering multiple objectives, including resource allocation, in decision-making processes[182].

Recent developments in expert systems have focused on integrating multiple factors for comprehensive wildfire management. A 2017 meta-analysis on the effects of thinning and burning on understory vegetation in North America provides valuable insights into the ecological impacts of forest management practices. Additionally, a 2019 statistical model for predicting PM2.5 for the western United States showcases the integration of air quality considerations into wildfire management. These developments reflect a trend towards more holistic risk assessment approaches, akin to NASA’s PRA framework[185].

The evolution of expert systems in wildland fire management mirrors the principles of NASA’s PRA framework, particularly in the areas of RIDM and CRM[187]. These systems increasingly incorporate ecological, health, and economic considerations alongside traditional fire behavior prediction and suppression strategies, reflecting a more comprehensive approach to risk management[192].

The importance of data in wildfire management is underscored in several reviews. One paper highlights the need for a reliable technical infrastructure to gather, process, disseminate, and archive fire weather data and forecasts. This emphasis on data collection and management is crucial for informed decision-making and aligns with the data-driven approach of NASA’s Probabilistic Risk Assessment (PRA) framework.

It is worth noting that while expert systems in wildland fire management have made significant strides, there remains a notable gap between the processes employed in Hazard and Operability Study (HAZOP) and traditional Probabilistic Risk Assessment (PRA) approaches, and those utilized in environmental Decision Support Systems (DSS). Environmental DSS, such as those used in wildfire management, often prioritize adaptability and real-time decision support over the more structured and systematic approaches found in HAZOP and PRA. This divergence presents an intriguing opportunity for future research to explore how the rigorous methodologies of HAZOP and PRA might be more fully integrated into environmental DSS to enhance their robustness and reliability in the face of complex, dynamic environmental challenges.

Expert Systems

Expert systems in wildland fire management have undergone significant evolution over the past few decades, incorporating diverse aspects of risk assessment, decision support, and resource management. These systems range from general forest management tools to specialized applications for fire prediction, health risk assessment, and economic optimization, reflecting a trend towards more integrated, multifaceted approaches in fire management.

The INFORMS-TX (Integrated Forest Resource Management System in Texas), developed in 1994 by the USDA Forest Service and Texas A&M University, represents a pioneering effort in integrating rule-based expert systems with Geographic Information Systems (GIS) through a relational database management system. This early integration laid the groundwork for more sophisticated forest resource management tools, potentially influencing modern systems such as InFORM.

As expert systems evolved, they began to address more specific regional contexts. For instance, an intelligent system for forest fire risk prediction and fire fighting management was developed for Galicia, Spain in 2003. This trend towards tailored solutions for different geographical areas underscores the importance of considering local factors in wildfire management, aligning with the principles of NASA’s Risk-Informed Decision Making (RIDM) framework, which emphasizes the need for context-specific risk assessments.

The BehavePlus Fire Modeling System, reviewed in 2013, exemplifies the ongoing advancement in fire behavior prediction tools. This review highlighted the necessity for continuous updates and improvements in modeling capabilities, reflecting the dynamic nature of wildfire management challenges. This approach resonates with NASA’s Continuous Risk Management (CRM) philosophy, which advocates for ongoing risk assessment and mitigation strategies.

Expert systems have also been applied to specific ecosystem management practices. FIRETOOL, developed in 1996, focused on the use of prescribed fires in Brazilian savannas. This application demonstrates the adaptability of expert systems to diverse ecological contexts and management strategies, a crucial aspect in the comprehensive risk management approach advocated by NASA’s PRA framework.

Health considerations have become increasingly integrated into wildfire management expert systems. Studies on health risks for wildland firefighters due to chronic smoke exposure (2004) and firefighters’ exposure risks to bushfire smoke (2011) highlight the incorporation of occupational health considerations into fire management practices. This broadening scope aligns with the holistic risk assessment approach of NASA’s PRA framework, which considers multiple facets of risk in complex systems.

Economic aspects of wildfire management have also been addressed through expert systems. A forest management risk insurance model developed in 2006 for coniferous stands in southwest Germany introduced economic risk management tools to the forestry sector. Similarly, a 2010 study on the economic optimization of wildfire intervention activities represents a significant step towards integrating cost-effectiveness into fire management decision-making processes. These economic considerations parallel NASA’s RIDM approach, which emphasizes the importance of considering multiple objectives, including resource allocation, in decision-making processes.

Recent developments in expert systems have focused on integrating multiple factors for comprehensive wildfire management. A 2017 meta-analysis on the effects of thinning and burning on understory vegetation in North America provides valuable insights into the ecological impacts of forest management practices. Additionally, a 2019 statistical model for predicting PM2.5 for the western United States showcases the integration of air quality considerations into wildfire management. These developments reflect a trend towards more holistic risk assessment approaches, akin to NASA’s PRA framework.

The evolution of expert systems in wildland fire management mirrors the principles of NASA’s PRA framework, particularly in the areas of RIDM and CRM. These systems increasingly incorporate ecological, health, and economic considerations alongside traditional fire behavior prediction and suppression strategies, reflecting a more comprehensive approach to risk management.

It is worth noting that while expert systems in wildland fire management have made significant strides, there remains a notable gap between the processes employed in Hazard and Operability Study (HAZOP) and traditional Probabilistic Risk Assessment (PRA) approaches, and those utilized in environmental Decision Support Systems (DSS). Environmental DSS, such as those used in wildfire management, often prioritize adaptability and real-time decision support over the more structured and systematic approaches found in HAZOP and PRA. This divergence presents an intriguing opportunity for future research to explore how the rigorous methodologies of HAZOP and PRA might be more fully integrated into environmental DSS to enhance their robustness and reliability in the face of complex, dynamic environmental challenges.

Ranking Systems

The field of wildland fire management has seen significant advancements in recent decades, with three major systems emerging as leaders in fire danger rating and decision support: the Canadian Forest Fire Danger Rating System (CFFDRS), the United States’ Wildland Fire Decision Support System (WFDSS), and the Australian Fire Danger Rating System (AFDRS). These systems have evolved to address the complex challenges of wildfire management, incorporating various factors such as weather, fuel conditions, and potential fire behavior.

The CFFDRS, developed in 1989, builds upon the Fire Weather Index (FWI) system from 1987. It comprises two main subsystems: the FWI and the Fire Behavior Prediction (FBP) system. The FWI integrates moisture, wind, and drought information to create a numerical rating of fire intensity, serving as a standalone general index of fire danger. This information is then fed into the FBP to predict fire spread patterns. Additionally, the FWI data is used in the Fire Occurrence Prediction System to forecast fire ignition locations. The CFFDRS forms the foundation for larger systems, such as the W.I.S.E. project (formerly Prometheus), which acts as a front-end Decision Support System (DSS) endorsed by the Canadian Interagency Forest Fire Centre.

In the United States, the WFDSS has become a crucial tool for fire management. It incorporates various models and data sources to provide comprehensive decision support for fire managers. The system builds upon earlier frameworks like FIRETOOL, developed in 1996 for prescribed fires in Brazilian savannas. The WFDSS integrates elements from the National Fire Danger Rating System (NFDRS), which underwent significant revisions in 1978, 1985, and 1988. This system emphasizes the importance of GIS in wildfire management workflows and addresses a wide range of objectives, including timber management, protection, recreation, and climate considerations.

The AFDRS, launched in 2022, represents a major overhaul of Australia’s fire danger rating approach. Prior to this, the system had remained largely unchanged since 1974, primarily based on McArthur’s metrics. The new AFDRS addresses the need for a more comprehensive and adaptable system in the face of changing landscapes and climate conditions. It consists of eight components tailored to different vegetation types: Forest, Grassland, Northern Grassland (Savanna), Spinifex, Mallee-Heath, Shrubland, Buttongrass, and Pine. The AFDRS aims to balance simplicity of use with the ability to provide detailed information when needed, incorporating factors such as fire behavior, ignition likelihood, suppression capacity, and potential impacts.

These three systems share common goals of improving fire danger assessment and supporting decision-making processes. They all emphasize the importance of integrating multiple data sources, including weather information, fuel conditions, and historical fire data. The systems also recognize the need for continuous improvement and adaptability to changing environmental conditions and management needs.

Beyond these major systems, numerous other approaches and tools have been developed to address specific aspects of wildfire management. For instance, the INFORMS-TX (Integrated Forest Resource Management System in Texas), created by the USDA Forest Service and Texas A&M University, integrates a rule-based expert system with GIS through a relational database management system. This system demonstrates the potential for combining expert knowledge with spatial data to enhance forest resource management.

Probabilistic and statistical models have also gained prominence in wildfire risk assessment. Bayesian approaches have been applied in various contexts[105][106][148], offering a framework for incorporating uncertainty and updating risk assessments as new information becomes available. Logistic regression models have been used to assess the probability of crown fire initiation based on fire danger indices, providing valuable insights for fire behavior prediction.

The development of these systems and models reflects a growing recognition of the complexity of wildfire management and the need for sophisticated tools to support decision-making. Many recent efforts have focused on addressing the challenges posed by climate change, acknowledging that the domain of wildfire management is shifting as environmental conditions evolve. This has led to increased emphasis on adaptive management approaches and the integration of climate projections into risk assessment models.

Human factors and risk perception have also emerged as important considerations in wildfire management. Studies have explored the gap between expert assessments and public perception of fire risk, highlighting the need for effective communication strategies and public engagement in fire management efforts. This human-centered approach is increasingly being incorporated into decision support systems and risk communication frameworks.

In conclusion, the field of wildland fire management has seen significant advancements in recent years, with major systems like the CFFDRS, WFDSS, and AFDRS leading the way. These systems, along with numerous specialized tools and models, provide fire managers with unprecedented capabilities for assessing fire danger, predicting fire behavior, and making informed decisions. As the field continues to evolve, there is a growing emphasis on adaptability, integration of diverse data sources, and consideration of human factors in fire management strategies. The ongoing development of these systems and approaches will be crucial in addressing the complex challenges posed by wildfires in a changing climate.

The field of wildland fire management has witnessed significant advancements in recent decades, with three major systems emerging as leaders in fire danger rating and decision support: the Canadian Forest Fire Danger Rating System (CFFDRS), the United States’ Wildland Fire Decision Support System (WFDSS), and the Australian Fire Danger Rating System (AFDRS). These systems have evolved to address the complex challenges of wildfire management, incorporating various factors such as meteorological conditions, fuel characteristics, and potential fire behavior.

Canadian Forest Fire Danger Rating System (CFFDRS)

The CFFDRS, developed in 1989, builds upon the Fire Weather Index (FWI) system from 1987. It comprises two primary subsystems:

1. Fire Weather Index (FWI)
2. Fire Behavior Prediction (FBP) system

The FWI integrates moisture, wind, and drought information to generate a numerical rating of fire intensity, serving as a standalone general index of fire danger. This information is subsequently utilized by the FBP to predict fire spread patterns. Additionally, the FWI data informs the Fire Occurrence Prediction System to forecast fire ignition locations.

The CFFDRS forms the foundation for larger systems, such as the W.I.S.E. project (formerly Prometheus), which functions as a front-end Decision Support System (DSS) endorsed by the Canadian Interagency Forest Fire Centre.

United States Wildland Fire Decision Support System (WFDSS)

In the United States, the WFDSS has become a crucial tool for fire management. It incorporates various models and data sources to provide comprehensive decision support for fire managers. The system builds upon earlier frameworks like FIRETOOL, developed in 1996 for prescribed fires in Brazilian savannas.

The WFDSS integrates elements from the National Fire Danger Rating System (NFDRS), which underwent significant revisions in 1978, 1985, and 1988. This system emphasizes the importance of Geographic Information Systems (GIS) in wildfire management workflows and addresses a wide range of objectives, including timber management, protection, recreation, and climate considerations.

Australian Fire Danger Rating System (AFDRS)

The AFDRS, launched in 2022, represents a major overhaul of Australia’s fire danger rating approach. Prior to this, the system had remained largely unchanged since 1974, primarily based on McArthur’s metrics. The new AFDRS addresses the need for a more comprehensive and adaptable system in the face of changing landscapes and climate conditions.

It consists of eight components tailored to different vegetation types:

1. Forest
2. Grassland
3. Northern Grassland (Savanna)
4. Spinifex
5. Mallee-Heath
6. Shrubland
7. Buttongrass
8. Pine

The AFDRS aims to balance simplicity of use with the ability to provide detailed information when needed, incorporating factors such as fire behavior, ignition likelihood, suppression capacity, and potential impacts.

Integration with NASA’s PRA Framework

These wildland fire management systems share conceptual similarities with NASA’s Probabilistic Risk Assessment (PRA) framework, particularly in the areas of Risk-Informed Decision Making (RIDM) and Continuous Risk Management (CRM). Both domains emphasize the importance of integrating multiple data sources, quantifying uncertainties, and supporting informed decision-making processes.

The CFFDRS, WFDSS, and AFDRS all incorporate elements of risk assessment and decision support, aligning with the RIDM approach. They provide fire managers with tools to evaluate potential risks and make informed decisions based on quantitative and qualitative data. Similarly, the adaptive nature of these systems, particularly evident in the AFDRS’s modular design, reflects the principles of CRM, allowing for continuous improvement and adaptation to changing conditions.

Additional Approaches and Tools

Beyond these major systems, numerous other approaches and tools have been developed to address specific aspects of wildfire management. For instance, the INFORMS-TX (Integrated Forest Resource Management System in Texas), created by the USDA Forest Service and Texas A&M University, integrates a rule-based expert system with GIS through a relational database management system. This system demonstrates the potential for combining expert knowledge with spatial data to enhance forest resource management.

Probabilistic and statistical models have also gained prominence in wildfire risk assessment. Bayesian approaches have been applied in various contexts[105][106][148], offering a framework for incorporating uncertainty and updating risk assessments as new information becomes available. Logistic regression models have been used to assess the probability of crown fire initiation based on fire danger indices, providing valuable insights for fire behavior prediction.

Human Factors and Risk Perception

Human factors and risk perception have emerged as important considerations in wildfire management. Studies have explored the gap between expert assessments and public perception of fire risk, highlighting the need for effective communication strategies and public engagement in fire management efforts. This human-centered approach is increasingly being incorporated into decision support systems and risk communication frameworks.

Gap Analysis: HAZOP, PRA, and Environmental DSS

It is worth noting that while the wildland fire management systems discussed here share conceptual similarities with NASA’s PRA framework, there remains a notable gap between the processes employed in Hazard and Operability Study (HAZOP) and traditional PRA approaches, and those utilized in environmental Decision Support Systems (DSS). Environmental DSS, such as those used in wildfire management, often prioritize adaptability and real-time decision support over the more structured and systematic approaches found in HAZOP and PRA. This divergence presents an opportunity for future research to explore how the rigorous methodologies of HAZOP and PRA might be more fully integrated into environmental DSS to enhance their robustness and reliability in the face of complex, dynamic environmental challenges.