Challenge background
Climate change is one of the most pressing global challenges, leading to extreme weather events, rising sea levels, biodiversity loss, and resource scarcity. Bhutan is increasingly vulnerable to climate-related risks such as glacial lake outburst floods (GLOFs), changing monsoon patterns, and agricultural instability. These impacts are particularly pronounced in Bhutan’s rural and mountainous regions, where communities often lack the necessary resources, infrastructure, and early warning systems to effectively mitigate and adapt to these changes. Despite global efforts, addressing climate change remains difficult due to complex data requirements, slow response times, and the need for scalable solutions. Artificial Intelligence (AI) presents a transformative opportunity to tackle climate change. AI-powered solutions can enhance climate monitoring, predict environmental changes, and optimize resource management to support sustainability efforts—even in geographically challenging areas like Bhutan. However, implementing AI in climate action also presents challenges, such as ensuring data accuracy in remote regions, minimizing the energy consumption of AI models, and maintaining ethical transparency in decision-making.
The problem
Our goal is to develop scalable, data-driven solutions that improve climate risk mitigation, particularly in underserved and vulnerable communities, while ensuring ethical transparency and minimizing the energy consumption of AI models.
Goal of the project
- Gather and integrate climate-related data from multiple sources to ensure comprehensive coverage.
- Clean, process, and structure the data for analysis and model training.
- Develop machine learning models to predict climate-related events and optimize resource management.
- Validate model performance through rigorous testing and fine-tuning for accuracy and robustness.
- Deploy the AI platform for real-time use and monitor its effectiveness in mitigating climate risks.
Project timeline
- 1
Week 1
Week 1-2: Discovery & Prototype Development
- Define project scope, requirements, and constraints with stakeholders.
- Review current systems and integration points.
- 2
Week 2
Week 1-2: Discovery & Prototype Development
- Explore and prototype AI models for route optimization.
- Develop initial prototype with available datasets.
- 3
Week 3
Week 3-4: AI Model Testing & Iteration
- Experiment with AI models
- Integrate real-time constraints and test with initial data.
- 4
Week 4
Week 3-4: AI Model Testing & Iteration
- Establish performance metrics and conduct preliminary testing.
- 5
Week 5
Week 5-6: Model Refinement & Final Integration
- Refine the model based on feedback and performance results.
- Optimize for scalability and dynamic routing.
- 6
Week 6
Week 5-6: Model Refinement & Final Integration
- Conduct final testing and prepare for deployment.
- Finalize integration or deploy as an API and provide user training.
What you'll learn
- Climate Change Prediction and Early Warning Systems: analyze historical climate data and predict extreme weather events such as floods, hurricanes, and droughts. By using machine learning to detect patterns in climate data, these systems can provide early warnings to communities, enabling better preparedness and response.
- Mitigating risks: Citizens within the risk region receiving timely SMS alerts about imminent extreme weather conditions.
- Local Government and Municipal Councils: Utilizing predictive data to enhance disaster preparedness and response strategies.
- Rescue Organizations: Using early warnings to mobilize resources and coordinate rescue operations efficiently. Food safety and Supply Chain Risk: Adapting agricultural practices based on weather predictions to mitigate crop damage.