Brahmaputra-CoPilot: A Multilingual Edge-AI Framework for Flood and Landslide Early-warning across Northeast India

Abstract

One of the major hazards for Northeast India is floods and landslides. While hydrometeorological forecasting capabilities have improved, last-mile advisory delivery — actionable, local language, and low-connectivity capable — is still inadequate. Hence, we present Brahmaputra-CoPilot, it is a conceptual multilingual edge-AI framework. It fuses rainfall nowcasts, river-gauge telemetry, SAR flood masks, and DEM-derived terrain features into actionable, time-bound advisories delivered via SMS/voice in Assamese–Hindi–English code-mix. The tri-agent conceptual design (Data Agent, GeoReasoning Agent, Communication Agent) couples physics-informed hazard scoring, lightweight ML decision logic, and retrieval-augmented generative language models optimized for edge inference (int8 quantized models, 1–3B parameters). We have performed a comprehensive retrospective simulation study (2017–2025) using public rainfall and gauge archives, Sentinel-1 SAR rasters, and DEM layers to evaluate event-centric precision, recall, lead time, and system latency. We discuss design tradeoffs, ethical safeguards, and a staged pilot plan. The paper (literature from 2017-2025) synthesizes disaster-informatics, edge-AI, and multilingual NLP and suggests a pragmatic path to field validation and scale-up.

Introduction

The Brahmaputra River Basin in South Asia is highly vulnerable to seasonal floods and landslides, but conventional forecasts often fail to provide localized, actionable, and multilingual advisories for affected communities. Brahmaputra-CoPilot is a conceptual, tri-agent system designed to address this gap by:

1. Fusing hydrometeorological and terrain data for spatiotemporal risk scoring,

2. Translating risk into household- and block-level actionable instructions through geo-reasoning, and

3. Delivering trusted, multilingual advisories via a retrieval-augmented LLM that operates offline on low-end devices.

Simulations using historical events (2017–2025) show high precision (0.85) and recall (0.88) with low inference latency (~1.9 s), comparable to cloud-based systems. The framework emphasizes human-centered communication, adaptive thresholding, and cultural localization, though real-world deployment and mobile app integration remain future steps. Ethical considerations include trust, accountability, data privacy, and community co-design. Planned next steps involve field validation, MVP development, regional scaling, and integration with local disaster management agencies and NGOs.

Conclusion

Brahmaputra-CoPilot is a conceptual baseline for AI-assisted multilingual flood communication. Although with limitations, using only open datasets and edge computation, it achieves robust simulated precision and recall. With further efforts like field validation, data partnerships, and system integration form the natural continuation. Along with pilot deployment, cross-basin generalization, and reinforcement-learning feedback from user responses, this present work which is positioned as a conceptual and simulation-level feasibility study demonstrating how AI, data fusion, and multilingual NLP can converge for disaster resilience will be a success and will make difference in lives of northeast people.

References

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Copyright

Copyright © 2025 Priyanka Nayak, Komal Dudam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.