Building ClimateSense: Leveraging AI to Combat Climate Change in Bhutan

The problem

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. Current challenges include ensuring data accuracy in remote regions, minimizing the energy consumption of AI models, and maintaining ethical transparency in decision-making. The lack of integrated climate intelligence systems prevents timely interventions and coordinated responses across Bhutan’s diverse ecosystems and vulnerable communities.

Impact of the Problem:

• 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.
• Citizens within risk regions: 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.
• Agricultural Communities: Adapting farming practices based on weather predictions to mitigate crop damage and ensure food safety and supply chain continuity.
• Glacial Lake Monitoring: Real-time GLOF risk assessment for high-altitude communities.
• Water Resource Management: Optimizing water allocation during seasonal variations.
• Infrastructure Planning:  Assess climate vulnerability for roads, bridges, hospitals, and schools, especially in landslide-prone zones.

The goals

• 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.

• Week 1-2: Discovery & Prototype Development
  • Define project scope, requirements, and constraints with stakeholders.
  • Review current systems and integration points.
  • Explore and prototype AI models for Climate.
  • Develop initial prototype with available datasets.
• Week 3-4: AI Model Testing & Iteration
  • Integrate real-time constraints and test with initial data
  • Establish key performance metrics and conduct preliminary testing
• Week 5-6: Model Refinement & Final Integration
  • Refine model based on stakeholder feedback
  • Optimize for scalability
  • Test with larger data sets and improve handling of constraints
  • Iterate on model performance based on testing results
• Week 6-8: Final Integration, Deployment, & Reporting
  • Finalize integration or deploy as an API
  • Conduct final system tests and ensure seamless deployment
  • Prepare and deliver comprehensive documentation
  • Provide user training for field agents and managers

Outcomes 

• AI Model: A machine learning model that predicts extreme weather conditions (floods, heavy rains, heat waves).
• API: An API for accessing the model’s predictions.
• Dashboard: A user-friendly dashboard for visualizing predictions and system performance.