An AI Powered System for Translating Early Warning Alerts into Common Alert Protocol Messages and Ensuring Inclusive Delivery in Indigenous Language and Local Dialect

Problem:
Bangladesh is selected as our challenge implementation area. Early warnings for hydrometeorological extremes are issued by the Bangladesh Meteorological Department (BMD) and the Flood Forecasting and Warning Centre (FFWC), with approval from the Department of Disaster Management (DDM). These warnings are shared via TV, radio, and mobile phones in some project implemented areas. However, BMD’s messages are often too technical for grassroots communities with low disaster literacy. The system also overlooks the needs of persons with disabilities and linguistic minorities. Our work will address these gaps by translating early warnings into simple, accessible language and formats tailored to local communities.

A major limitation of current early warning systems in Bangladesh is that messages are often overly technical and difficult for local communities to understand. Our solution addresses this challenge by converting early warnings into the Common Alerting Protocol (CAP) format, which standardizes key details such as hazard type, intensity, likely impacts, and recommended actions. Using AI, these CAP messages will be translated into indigenous languages and simplified for clarity. They will be delivered via voice SMS and sign language through mobile devices, using location-specific language maps that ensure accessibility even for persons with disabilities. This inclusive and locally tailored communication approach ensures that vulnerable and marginalized populations not only receive alerts but also understand and act on them. By transforming complex forecasts into clear, actionable messages, our solution enhances the reach, inclusivity, and overall effectiveness of early warning systems, ultimately supporting better preparedness and risk reduction at the community level. Our target audience is mainly the marginalized and exposed population with low disaster literacy, living in hazard-prone remote areas. As our solution is designed for community use, we will test it in a vulnerable local community in a disaster-prone coastal area of Bangladesh. During the pre-test, we will consider the community’s specific needs and explore how to address them more effectively.

Solution:
Language barriers reduce EW effectiveness. Our AI system converts official alerts into CAP, translates them into local languages, and delivers clear warnings via voice or text messages.

Technical Requirements:

a. Mobile network tower location (BTRC),
b. Local dialect and language database
c. Geographic locations of ethnic groups
d. Connectivity needs with government agencies and other stakeholders-
i. Dept of Disaster Management
ii. Bangladesh Meteorological Department (DDM)
iii. Bangladesh Telecom Regulatory Commission (BTRC)
iv. Bangladesh Bureau of Statistics (BBS)
v. Local Administration
vi. Community-based organizations (CBOs)
e. Computing resources – Amazon cloud computing
f. Other dependencies – logistics and office operations (HR and Transport)

Operational Environment:

Urban / Metropolitan
Rural
Coastal
Inland

Licensing or Cost Structure:

Free to use

Ethical and Governance Considerations:

Ethically, we address privacy, bias, and consent using diverse datasets, anonymization, and community validation, following OECD, UNESCO, and EU AI guidelines. Location-based alerts may exclude low-literacy or offline users, while AI risks bias if minority groups are underrepresented. Our AI-powered system removes linguistic, cultural, and physical barriers by delivering early warnings in local dialects, using speech, radio, and phone calls for the illiterate, following an accessible design for disabilities, and engaging communities in inclusive co-design. Legal compliance is ensured via the Digital Security Act 2018, GDPR, and regular audits.