# From Black Box to Insight: Explainable AI for Extreme Event Preparedness

arXiv: https://arxiv.org/abs/2511.13712 · HTML: https://mlsanity.com/paper/2511.13712/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 65 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 15 of 15)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks](https://mlsanity.com/paper/1909.09586/) (2019)
- [Gated Transformer Networks for Multivariate Time Series Classification](https://mlsanity.com/paper/2103.14438/) (2021)
- [Analog forecasting of extreme-causing weather patterns using deep learning](https://mlsanity.com/paper/1907.11617/) (2019)
- [Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets](https://mlsanity.com/paper/1605.01156/) (2016)
- [Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean](https://mlsanity.com/paper/2306.05144/) (2023)
- [TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting](https://mlsanity.com/paper/2306.10940/) (2023)
- [Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data](https://mlsanity.com/paper/2503.14150/) (2025)
- [Explainable AI Integrated Feature Engineering for Wildfire Prediction](https://mlsanity.com/paper/2404.01487/) (2024)
- [Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational data](https://mlsanity.com/paper/2404.08613/) (2024)
- [AI for Extreme Event Modeling and Understanding: Methodologies and Challenges](https://mlsanity.com/paper/2406.20080/) (2024)
- [Evaluating Pavement Deterioration Rates Due to Flooding Events Using Explainable AI](https://mlsanity.com/paper/2507.01056/) (2025)
- [Explainable Earth Surface Forecasting under Extreme Events](https://mlsanity.com/paper/2410.01770/) (2024)
- [Extreme precipitation forecasting using attention augmented convolutions](https://mlsanity.com/paper/2201.13408/) (2022)

## Built on by (most cited first, 3 of 3)

- [TianJi:An autonomous AI meteorologist for discovering physical mechanisms in atmospheric science](https://mlsanity.com/paper/2603.27738/) (2026)
- [Modular Deep Learning Mechanisms for Auditable Next-Day Wildfire Spread Prediction](https://mlsanity.com/paper/2609.17763/) (2026)
- [Context-Aware Concept Distillation for Trustworthy Flood Prediction](https://mlsanity.com/paper/2607.23237/) (2026)

## Abstract (arXiv)

As climate change accelerates the frequency and severity of extreme events such as wildfires, the need for accurate, explainable, and actionable forecasting becomes increasingly urgent. While artificial intelligence (AI) models have shown promise in predicting such events, their adoption in real-world decision-making remains limited due to their black-box nature, which limits trust, explainability, and operational readiness. This paper investigates the role of explainable AI (XAI) in bridging the gap between predictive accuracy and actionable insight for extreme event forecasting. Using wildfire prediction as a case study, we evaluate various AI models and employ SHapley Additive exPlanations (SHAP) to uncover key features, decision pathways, and potential biases in model behavior. Our analysis demonstrates how XAI not only clarifies model reasoning but also supports critical decision-making by domain experts and response teams. In addition, we provide supporting visualizations that enhance the interpretability of XAI outputs by contextualizing feature importance and temporal patterns in seasonality and geospatial characteristics. This approach enhances the usability of AI explanations for practitioners and policymakers. Our findings highlight the need for AI systems that are not only accurate but also interpretable, accessible, and trustworthy, essential for effective use in disaster preparedness, risk mitigation, and climate resilience planning.