An Earth System Digital Twin for Wildfire: Predicting Wildfire Progression and Behavior, and Its Downstream Impacts on Air Quality

This proposal presents an Earth System Digital Twin (ESDT) for Wildfire, delivering an advanced and integrated system designed to enhance the accuracy, efficiency, and real-time responsiveness of wildfire forecasting and management. This wildfire ESDT utilizes a comprehensive set of technologies, including novel AI-based frameworks with near real-time high-resolution predictive models to forecast wildfire spread and progression in the future, as well as its downstream impact on air quality. Wildfires in North America have become increasingly prevalent and severe, posing significant threats to human health, the environment, and the economy. The smoke generated by wildfires contributes to hazardous air quality, exposing people to harmful pollutants that can exacerbate respiratory conditions and lead to long-term health issues. Beyond human impacts, wildfires have devastating effects on ecosystems, causing habitat destruction, loss of biodiversity, and releasing vast amounts of carbon dioxide into the atmosphere, further exacerbating climate change. Despite notable progress in wildfire modeling and mitigation technologies in recent years, understanding and predicting wildfire behavior and progression in near real-time, along with its adverse impacts, remains highly challenging and complex. The proposed Wildfire ESDT includes: 1- A unified data platform integrating the latest available information from satellite and in situ observations to visualize the current status of wildfires and air quality (What-Now). 2- High-resolution and near real-time AI-based predictive models to forecast active wildfire spread, progression, and trajectory in the future, and its short-term and long-term impacts on air quality (What-Next). 3- Impact Assessment models and tools that provide projections and predictions for different response scenarios, actions, and conditions, and let the user explore scenario-based assessments (What-If). 4- Low latency and user-friendly visualization models and interactive user-interfaces (including VR/AR) to visualize current and future status of fire and air quality, potential response scenarios (What-Now, What-Next, What-If), and uncertainty quantifications. Building upon our prior initiatives and successful AIST projects including Predicting What We Breathe (PWWB), Fire Alarm, and Air Quality Analytic Collaborative Framework (AQ-ACF), this project aims to incorporate unique data processing algorithms, novel AI-based predictive models, and state-of-the-art machine learning (ML) algorithms for precise forecasting of active wildfire behavior and progression over time, as well as its air quality impacts. This project will also address the challenges of integrating large-scale datasets from various sources by establishing a unified system for processing satellite observation, ground-based data, and land information for comprehensive analysis and accurate forecasting. Furthermore, the project places a strong emphasis on user experience by incorporating interactive visualization methods. These methods serve as a dynamic interface, delivering the obtained insights and forecast outcomes to the user, and facilitating a more intuitive and informed decision-making process. The proposed Wildfire ESDT will significantly support firefighters, emergency responders, and various stakeholders in optimizing resource allocation, setting priorities, and executing targeted responses to wildfires. It also plays a pivotal role in efficiently evacuating individuals to secure locations, ensuring a prompt and coordinated approach to saving lives during wildfire incidents. Additionally, It serves as a vital tool for policymakers and researchers, enhancing their ability to analyze wildfire patterns, providing data-driven insights into wildfire behavior, and facilitating informed decision-making and effective management strategies.

Expand current definitions of modeling and leverage state-of-the-art computer and information science for innovating advanced modeling techniques as well as new technologies and frameworks that will be essential in the development of Earth System Digital Twins

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