Seeing Through the Smoke: AI Explains Wildfire Risk

Author: Denis Avetisyan

New research demonstrates how explainable AI is unlocking the ‘black box’ of wildfire prediction models to build greater trust in forecasts and improve disaster preparedness.

U.S. extreme disaster events have incurred increasingly significant costs over time, reflecting a growing financial burden associated with these occurrences.

This review details the application of SHAP values and time series analysis to deep learning models for wildfire prediction, highlighting key features and aligning with FAIRUST principles.

Despite advances in artificial intelligence for forecasting, the “black-box” nature of many models hinders their adoption in critical decision-making for escalating extreme events. This paper, ‘From Black Box to Insight: Explainable AI for Extreme Event Preparedness’, investigates how explainable AI (XAI) can bridge the gap between predictive accuracy and actionable understanding, using wildfire prediction as a case study. Our analysis, leveraging SHAP values, reveals key drivers of model behavior and demonstrates how increased transparency fosters trust and supports informed responses. Can XAI unlock the full potential of AI for proactive disaster preparedness and build more resilient communities in a changing climate?

Understanding the Evolving Wildfire Threat

The increasing frequency and intensity of wildfires represent a growing global crisis, impacting not only the health of vast ecosystems but also the safety and livelihoods of communities worldwide. Driven by factors like climate change, land management practices, and increasing human-wildland interface, these events cause substantial economic damage, displace populations, and release significant amounts of carbon into the atmosphere, further exacerbating climate challenges. Consequently, there is an urgent need for enhanced wildfire prediction capabilities – systems that can accurately forecast fire ignition, spread, and behavior. Improved prediction isn’t merely about anticipating where fires might start, but also understanding how they will evolve, allowing for proactive mitigation strategies, efficient resource allocation, and ultimately, more effective protection of both natural landscapes and human settlements.

Predicting wildfire behavior isn’t simply a matter of current conditions; it fundamentally depends on deciphering the intricate interplay of time and season. Wildfire regimes aren’t static; they exhibit strong temporal dynamics, meaning past events significantly influence future risk. Researchers are discovering that seasonal patterns – the buildup of fuel moisture in spring, the peak of dryness in summer, and the influence of autumn winds – create predictable windows of vulnerability. However, these patterns are becoming increasingly disrupted by climate change, leading to longer fire seasons and more extreme events. Sophisticated models now incorporate decades of historical data, alongside real-time monitoring, to identify these temporal trends and forecast potential ignition points and spread rates. Understanding when and where conditions align to create peak fire risk is proving crucial for proactive resource allocation and effective prevention strategies.

Current wildfire prediction models often fall short due to their limited ability to represent the complex, non-linear interactions that govern fire behavior. These models frequently rely on linear assumptions or simplified representations of crucial factors like wind patterns, fuel moisture, and topography. However, wildfire spread isn’t a straightforward process; small changes in any one of these variables can trigger disproportionately large effects on fire intensity and direction. For instance, a sudden shift in wind speed, combined with dry vegetation and steep slopes, can create a feedback loop accelerating fire growth in unpredictable ways. This complexity means traditional approaches, while useful for broad-scale risk assessment, struggle to accurately forecast the precise timing, location, and intensity of rapidly evolving wildfires, resulting in substantial forecasting errors and hindering effective mitigation efforts.

Leveraging Artificial Intelligence for Wildfire Forecasting

The application of AI-powered forecasting tools to spatiotemporal event prediction, specifically wildfires, is gaining traction due to limitations in traditional methods. These tools utilize machine learning algorithms to analyze complex datasets incorporating variables such as weather patterns, vegetation density, topography, and historical fire occurrences. This data-driven approach enables the identification of high-risk areas and provides probabilistic forecasts of fire ignition and spread. Consequently, resource allocation for preventative measures and suppression efforts can be optimized, and early warning systems can be improved, ultimately leading to increased predictive accuracy compared to conventional statistical modeling and expert judgment.

Random Forest and XGBoost are gradient boosting algorithms commonly utilized in predictive modeling due to their ability to handle complex datasets and identify non-linear relationships. These algorithms function by creating an ensemble of decision trees, where each tree is trained on a subset of the data and features. Historical data, including meteorological conditions such as temperature, humidity, and wind speed, alongside topographical features and fuel load, are used as inputs to identify risk factors associated with spatiotemporal events. Feature importance metrics within these algorithms quantify the contribution of each input variable to the prediction, allowing for the identification of key drivers and improved model interpretability. Their performance is often evaluated using metrics like precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC).

Model performance was evaluated using two datasets: Mesogeos and California Wildfires. On the Mesogeos Dataset, a peak accuracy of 87.53% was achieved. Performance on the California Wildfires Dataset yielded a peak accuracy of 78.71%. These results were obtained utilizing a Transformer model, indicating its capacity for effective wildfire prediction when trained on relevant historical data. The demonstrated accuracy levels suggest a viable pathway for integrating AI-driven tools into wildfire risk assessment and mitigation strategies.

Employing SHAP (SHapley Additive exPlanations) values for feature selection yielded a 3.75% improvement in model accuracy compared to a baseline approach utilizing the ten least important features. This result demonstrates the benefit of interpretable AI techniques in enhancing predictive performance. SHAP values assign each feature an importance score based on its contribution to the model’s output, allowing for the identification and prioritization of the most impactful variables. By focusing on these key features, the model’s ability to accurately predict outcomes is demonstrably improved, while also providing insight into the factors driving those predictions.

SHAP value analysis of the LSTM model on the Mesogeos dataset reveals the average feature contributions to its predictions.

The Importance of Transparency in AI-Driven Fire Prediction

Advanced Artificial Intelligence models, increasingly utilized in complex applications such as wildfire prediction, often function as ‘Black-Box AI Models’. This designation refers to the opacity of the model’s internal workings, meaning the specific factors and weighting applied to arrive at a given prediction are not readily discernible. While these models can achieve high levels of predictive accuracy, the lack of transparency poses challenges for understanding why a particular prediction was made. This limited insight restricts the ability to validate the model’s reasoning, identify potential biases, and build confidence in its outputs, hindering effective risk assessment and mitigation strategies in critical scenarios.

The opacity of many advanced AI models, often termed ‘black box’ systems, presents significant challenges to building confidence in their outputs and implementing effective preventative measures. Without clear understanding of the factors driving predictions – such as those used in wildfire forecasting – stakeholders are less likely to accept and act upon model recommendations. This diminished trust can lead to delayed or inadequate risk mitigation, potentially exacerbating negative outcomes. Specifically, the inability to trace a prediction back to its causal features hinders the validation process, making it difficult to identify and correct biases or errors within the system and impeding proactive strategies for resource allocation and public safety.

The application of FAIRUST principles – Findability, Accessibility, Interoperability, Reusability, Usability, Security, and Trustworthiness – is essential for responsible AI deployment in wildfire forecasting. These principles ensure that AI models and the data they utilize are readily discoverable and usable by relevant stakeholders, including fire management agencies and researchers. Accessibility encompasses not only data access but also the provision of clear documentation and understandable explanations of model outputs. Interoperability facilitates integration with existing forecasting systems, while Reusability promotes the adaptation of models to new regions or scenarios. Prioritizing Security safeguards data integrity and prevents malicious manipulation, and ultimately, adherence to these principles fosters Trustworthiness in AI-driven wildfire predictions, enabling informed decision-making and effective risk mitigation.

SHAP (SHapley Additive exPlanations) values provide a method for interpreting the output of any machine learning model. Implementation of SHAP explanations in wildfire prediction models has demonstrated a quantifiable improvement in predictive accuracy, up to 3.30% compared to models without SHAP-based analysis. Beyond accuracy gains, SHAP values facilitate the identification of key predictive features; analysis consistently indicates that temperature-related features are the most influential factors in determining model predictions. This insight allows for focused model refinement and increased confidence in the factors driving wildfire risk assessments.

Implementation of SHAP-guided feature selection yielded a measurable increase in training efficiency. Specifically, model training time was reduced by 3.86 seconds per epoch. This improvement stems from the reduction in computational load associated with processing a smaller, more relevant feature set, as identified through SHAP value analysis. The reduction in training time allows for faster model iteration and experimentation, ultimately contributing to a more agile development process and potentially enabling real-time or near-real-time wildfire prediction capabilities.

The pursuit of reliable wildfire prediction, as detailed in this study, necessitates more than just accurate forecasts; it demands comprehension. This aligns with John McCarthy’s observation that, “It is better to solve a problem which seems insoluble than to work on a problem which is soluble.” The application of SHAP values to deep learning models isn’t simply about identifying important features like temperature and precipitation; it’s about dismantling the ‘black box’ and fostering a deeper understanding of the system’s behavior. Just as infrastructure should evolve without rebuilding the entire block, these XAI techniques allow for incremental improvements and increased trust in the model without discarding valuable predictive power. This approach acknowledges that a system’s structure fundamentally dictates its behavior, a principle central to effective disaster preparedness.

Beyond the Horizon

The pursuit of intelligibility in forecasting extreme events, as demonstrated by the application of SHAP values to wildfire prediction, reveals a familiar pattern. Each illuminated feature, each quantified importance, is merely a node in a vastly more complex network. The elegance of a seemingly simple explanation belies the inherent difficulty of truly understanding a chaotic system. Every new dependency – every feature selected, every parameter tuned – is the hidden cost of freedom, a narrowing of potential futures in exchange for a localized, probabilistic glimpse.

Future work must address not only what the model highlights, but why those features gain prominence within the specific structural constraints of the chosen algorithm. A focus on algorithmic transparency, coupled with rigorous sensitivity analysis, will be crucial. The FAIRUST principles offer a valuable framework, but genuine accountability demands more than just post-hoc explanations; it requires a proactive assessment of inherent biases and limitations embedded within the model’s architecture.

Ultimately, the goal is not simply to predict, but to build systems resilient to the inevitable uncertainties. The true test lies not in achieving higher accuracy on historical data, but in gracefully adapting to the unforeseen, acknowledging that perfect knowledge remains an asymptotic ideal. The quest for explainability, therefore, is less about opening the ‘black box’ and more about understanding the limitations of the container itself.

Original article: https://arxiv.org/pdf/2511.13712.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

Understanding the Evolving Wildfire Threat

Leveraging Artificial Intelligence for Wildfire Forecasting

The Importance of Transparency in AI-Driven Fire Prediction

Beyond the Horizon

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