Data-Driven Observing Network Design for the Great Lakes and Southern Ocean

Overview

The Observing Network Design project aims to leverage data-driven tools to optimize the design and operation of environmental monitoring networks in both the Great Lakes region and the Southern Ocean. By using advanced data science and machine learning techniques, the goal is to improve how we collect and interpret environmental data, ultimately enhancing our ability to monitor and respond to changes in these vital ecosystems.

Key Objectives:

• Develop methods to optimize sensor placement and network configuration
• Enhance monitoring of key environmental variables such as temperature, salinity, and nutrient concentrations
• Apply machine learning algorithms to analyze environmental data and inform network design decisions
• Support long-term monitoring strategies for climate change adaptation and ecosystem management

My Role:

As part of this project, I lead the development of data-driven methodologies for designing observing systems that are both efficient and effective. My work involves integrating machine learning tools to assess the most strategic locations for deploying sensors, based on historical and real-time environmental data.

Impact:

By improving the design of environmental monitoring networks, this project aims to enhance our ability to detect changes in the ecosystem, improve predictive capabilities for climate-related events, and support policy decisions related to environmental conservation and management.

Related Publications:

• [Link to any relevant paper or publication, if applicable]

Future Directions:

We plan to expand this work by incorporating more diverse datasets, including satellite observations and real-time sensor data, to refine our methods and ensure the networks are adaptable to evolving environmental conditions.