---
title: "Context-Aware Concept Distillation for Trustworthy Flood Prediction"
authors:
  - "Eli Levinkopf"
  - "Efrat Morin"
  - "Claudia V. Goldman"
doi: "10.24963/ijcai.2026/810"
url: "https://ijcai.org/proceedings/2026/810"
pdf: "https://ijcai.org/proceedings/2026/0810.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "7283-7291"
track: "AI and Social Good"
keywords:
  - "AI4G: AI Ethics, Trust, Fairness"
  - "AI4G: Humans and AI"
  - "AI4G: Multidisciplinary Topics and Applications"
  - "AI4G: Machine Learning"
---

# Context-Aware Concept Distillation for Trustworthy Flood Prediction

Eli Levinkopf, Efrat Morin, Claudia V. Goldman

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 7283-7291. DOI: [10.24963/ijcai.2026/810](https://doi.org/10.24963/ijcai.2026/810)

## Abstract

Effective flood risk management relies on accurate forecasting, yet the ”black box” nature of state-of-the-art Deep Learning models creates a barrier
to trust and accountability in high-stakes public safety decisions. While existing Explainable AI (XAI) methods offer local attributions, they fail to provide the verifiable, operationally meaningful causal narratives required by disaster response authorities. To address this societal challenge, we propose Context-Aware Concept Distillation (CACD), a framework developed in collaboration with domain experts to distill opaque LSTMs into interpretable, hydrology-aware surrogate models. We introduce an unsupervised pipeline to discover a ”Hydrological Language” and a Residual Hypernetwork that dynamically modulates these concepts based on static basin characteristics. Evaluated on 5,203 basins globally, our model achieves high fidelity (Median NSE 0.70), significantly outperforming black-box baselines (e.g., Multi Layer Perceptrons) on unseen future data. By demonstrating that human-interpretable concepts are sufficient to reconstruct flood dynamics, this work balances AI accuracy with the transparency required for responsible environmental decision-making.