Executive Summary
Climate change is intensifying the frequency and severity of extreme weather events such as floods, droughts, heatwaves, cyclones, and landslides. Vulnerable communities—particularly smallholder farmers, coastal populations, informal settlements, women, children, and indigenous groups—are disproportionately affected due to limited access to timely climate information, early warning systems, and adaptive capacity. Traditional climate forecasting and disaster response mechanisms often fail to reach last-mile populations or provide actionable, localized insights.
The proposed project, AI-Based Climate Risk Forecasting for Vulnerable Communities, aims to leverage artificial intelligence (AI), machine learning, remote sensing, and local data to deliver accurate, timely, and community-relevant climate risk forecasts. The project will integrate climate data, satellite imagery, historical hazard records, and socio-economic vulnerability indicators to generate early warnings and risk advisories that support preparedness, adaptation, and resilience-building at the community level.
Implemented over a three-year period, the project will strengthen disaster risk reduction (DRR), climate adaptation, and community resilience through inclusive digital tools, local capacity building, and partnerships with government agencies and humanitarian actors. The initiative aligns with SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and the Sendai Framework for Disaster Risk Reduction.
Background and Context
Climate-related disasters have increased significantly over the past decades, causing loss of lives, livelihoods, infrastructure, and development gains. While advances in climate science and data analytics have improved forecasting at national and global levels, many vulnerable communities still lack access to localized, understandable, and actionable climate risk information.
AI-based climate risk forecasting systems can process vast and complex datasets to identify patterns, predict hazards, and assess exposure and vulnerability at fine spatial and temporal scales. When combined with community engagement and effective communication channels, these technologies can empower communities to take early action, reduce losses, and strengthen resilience.
However, gaps remain in data integration, last-mile delivery, institutional coordination, and trust between communities and technology-driven systems. This project addresses these gaps through a people-centered, inclusive approach that combines advanced analytics with local knowledge and community-based implementation.
Problem Statement
Vulnerable communities face multiple, interconnected challenges:
- Limited access to timely and localized climate risk forecasts and early warnings
- Weak integration of climate data with social and vulnerability information
- Low capacity to interpret and act on technical climate information
- Inadequate early action mechanisms and preparedness planning
- Digital divides that exclude women, elderly, and marginalized groups
As a result, climate shocks lead to avoidable losses, displacement, food insecurity, and long-term vulnerability.
Project Goal and Objectives
Overall Goal
To reduce climate-related risks and enhance resilience of vulnerable communities through AI-powered climate risk forecasting and early warning systems.
Specific Objectives
- Develop AI-based models to forecast climate hazards and assess community-level risks.
- Deliver timely, accessible, and actionable climate risk advisories to vulnerable populations.
- Strengthen community capacity for preparedness, early action, and adaptive decision-making.
- Support local governments and institutions in integrating AI-driven climate intelligence into planning and response.
Target Beneficiaries
- Climate-vulnerable rural and urban communities
- Smallholder farmers, fishers, and pastoralists
- Women, children, elderly, and persons with disabilities
- Community-based organizations and local disaster committees
- Local government and disaster management authorities
Priority will be given to high-risk regions prone to floods, droughts, heat stress, cyclones, and landslides.
Project Components and Activities
- Component 1: AI-Based Climate Risk Forecasting System
- Component 2: Early Warning and Advisory Delivery
- Development of multi-channel dissemination systems (SMS, IVR, mobile apps, community radio)
- Localization of alerts in local languages and simple formats
- Impact-based forecasts and action-oriented advisories
- Integration with existing national early warning systems
- Component 3: Community Engagement and Capacity Building
- Component 4: Institutional Strengthening and Partnerships
- Collaboration with meteorological departments, disaster authorities, and research institutions
- Capacity building for local governments on AI-based climate planning tools
- Alignment with humanitarian and social protection programs
- Public–private partnerships with technology providers
- Component 5: Monitoring, Learning, and Adaptive Management
- Real-time monitoring of forecast accuracy and user response
- Community feedback mechanisms to improve usability and trust
- Documentation of lessons learned and best practices
- Knowledge sharing at national and regional levels
Implementation Strategy
The project will follow a phased and participatory approach. In the initial phase, baseline assessments and system design will be conducted in consultation with communities and stakeholders. The second phase will focus on pilot testing and refinement of AI models and delivery mechanisms. The final phase will scale up successful approaches and institutionalize systems within local and national frameworks.
Gender equality, social inclusion, and data ethics will be mainstreamed throughout implementation. Special measures will ensure accessibility for low-literacy users and marginalized groups.
Monitoring, Evaluation, and Learning (MEL)
Key indicators will include:
- Accuracy and timeliness of climate risk forecasts
- Number of communities receiving and using early warnings
- Adoption of early action and preparedness measures
- Reduction in climate-related losses and disruptions
- User satisfaction and inclusivity indicators
Baseline, midline, and endline evaluations will assess outcomes and impact.
Expected Outcomes and Impact
- Improved access to localized, reliable climate risk information
- Enhanced community preparedness and early action
- Reduced loss of lives, livelihoods, and assets from climate shocks
- Strengthened institutional capacity for climate risk management
- Increased resilience of vulnerable communities
Sustainability and Scalability
Sustainability will be ensured through institutional integration, capacity building, and open, interoperable systems. Partnerships with governments and regional institutions will support long-term operation and scaling to new hazards and regions.
Alignment with Global and National Frameworks
The project aligns with SDG 11 and SDG 13, the Sendai Framework for Disaster Risk Reduction, the Paris Agreement, and national climate adaptation and disaster management strategies.
Conclusion
AI-Based Climate Risk Forecasting for Vulnerable Communities offers a transformative approach to climate adaptation and disaster risk reduction. By combining advanced AI technologies with community-centered design and strong institutional partnerships, the project will enable proactive, inclusive, and resilient responses to climate change.
