SCC 107- AI AND CLIMATE CHANGE

Expected Outcomes By the end of this module, the learner is expected to in details demonstrate the following; AI application in science projects  Climate Modeling and Prediction: Our AI system helps climate models run more accurately and shows what future climate may look like.  Data Analysis and Pattern Recognition: AI systems can review big data collections to recognize natural patterns that explain climate change problems.  Weather Forecasting: Our weather prediction systems must become smarter to help us anticipate extreme weather situations.  Iceberg and Glacier Monitoring: Artificial Intelligence instruments collect glacier and iceberg motion data to show changing ice masses. AI in Climate Mitigation  Energy Efficiency: AI technology helps organizations save energy across their building networks plus factories and transportation systems.  Renewable Energy Management: AI systems work to control how renewable power enters the power distribution system.  Carbon Footprint Reduction: Our tools help different industries manage and cut their carbon emissions.  Smart Agriculture: Artificial Intelligence helps farmers produce more crops while making the environment healthier. AI in Climate Adaptation  Disaster Response: AI systems analyze and respond fast to floods and wildfire disasters.  Urban Planning: Our systems use Artificial Intelligence to develop cities and structures that can survive climate-related challenges.  Water Resource Management: The technology helps farmers and cities employ water wisely in both farming and urban settings.  Public Health: Artificial Intelligence tracks health problems connected to climate change patterns. Challenges and Considerations  Energy Consumption of AI: Our approach addresses both the heavy energy use and environmental concerns of AI computing systems.  Ethical and Social Implications: Our system should use artificial intelligence fairly to eliminate disadvantages in society.