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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 b","title":"Context-Aware Concept Distillation for Trustworthy Flood Prediction","url":"https://arxiv.org/abs/2607.23237","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.23237v1 Announce Type: cross \nAbstract: Effective flood risk management relies on accurate forecasting, yet the \"black box\" nature of stateof-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. 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