Community-Centric Enhanced Flood Preparedness and Response Plan in Tanguar Haor Region
Climate Adaptation & Resilience
- 50+ organizations
- 2024-2027
- 12 Publications
Lead: Dr. Sarah Johnson
Summary
Launched in February 2022 and ongoing, the Community-Centric Enhanced Flood Preparedness and Response Plan targets extreme marginalized localities in Bangladesh, particularly Tanguar Haor, to integrate disaster preparedness and response strategies. Focusing on socio-economic transformation, social inclusion, and climate resilience, it will soon launch ‘Tufaan’ (www.tufaan.org) to engage nationwide youth. Associated with Daahuk, this initiative builds resilient networks in flood-prone wetlands, drawing from work models like action-oriented research and stakeholder collaboration. It complements signature efforts such as the Teesta Basin Livelihood Programme, using multi-purpose adaptive boats for relief, education, and transport in char regions. By mobilizing communities and partners, it enhances disaster readiness, promotes cooperative models, and aligns with values of resilience and adaptability for sustainable, community-owned solutions in ecologically critical areas.
Key Objectives
- Climate Intelligence: Develop AI systems that forecast and mitigate climate risks in vulnerable regions.
- Social Impact Analytics: Use data to identify inequities and design targeted social interventions.
- Sustainable Development: Support environmental restoration and sustainable resource use through predictive modeling.
- Collaborative Innovation: Build partnerships across sectors to ensure ethical and inclusive AI implementation.
Key Objectives
50+ Organization
25+Countries of operation
100k+Beneficiaries reached
Key Challenges We Aim to Overcome
The project faces several challenges, including limited access to reliable and diverse data, ethical and privacy concerns in AI usage, and difficulties in ensuring transparency and fairness. Collaboration across global organizations can lead to coordination complexities, while technological and infrastructure gaps in some regions may hinder implementation
Data Accessibility and Quality
Obtaining reliable, diverse, and up-to-date datasets from different regions can be difficult
Ethical and Privacy Concerns
Ensuring transparency and fairness in AI decisions is crucial to prevent bias and misuse
Limited Technological Infrastructure
Many developing regions lack the resources, connectivity needed to effectively use AI solutions.
Project Highlights
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