Project Spotlight: Predictive Drought Modeling for Del-Co Water
An Academic-Industry Collaboration for Climate-Resilient Infrastructure
To build a more resilient water future, Ohio Wesleyan University (OWU) Data Lab has developed an advanced machine learning model for drought prediction for Del-Co Water Company . By blending deep environmental science with cutting-edge data analytics, this project translates complex climate data into actionable insights for public utility management.
The Collaborative Team
Our project leverages a unique cross-disciplinary framework, bridging academic research and real-world utility operations:
Environmental Domain Experts: Dr. Carolina Barbosa, Dr. Nathan Rowley, and two environmental studies students from the OWU Environment & Sustainability Department. Their team spearheaded the identification of critical climate indicators and environmental variables.
Data Science Architects: James O'Connor and Aryaa Subedi, serving as Data Science Research Assistants from OWU's data analytics program. They engineered the predictive pipelines and trained the machine learning algorithms.
Utility Partners & Stakeholders: Fred Milan and Frances Wiggins, serving as our primary points of contact at Del-Co Water, providing operational guardrails and industry expertise.
Project Framework & Methodology
The success of this initiative relies on a continuous feedback loop between environmental theory, data science, and utility operations.
A major challenge in climate modeling is isolating the data that truly matters. The Environment & Sustainability team identified the core environmental variablesnecessary to detect early-stage drought conditions.
James OConnor and Aryaa Subedi then ingested these variables to construct the predictive engine. Using advanced machine learning algorithms, they built a model capable of recognizing complex, non-linear climate patterns to forecast water scarcity well before it impacts infrastructure.
Current Status & Next Steps
This initial phase successfully established a robust Proof of Concept (PoC), validating that machine learning can accurately model localized drought conditions for Del-Co Water.
Phase 2: Fall Refinement:
Beginning this fall, we are launching the next iteration of the model to enhance its predictive accuracy and operational readiness.
Environmental Domain Experts: Dr. Carolina Barbosa, Dr. Nathan Rowley, and two environmental studies students from the OWU Environment & Sustainability Department. Their team spearheaded the identification of critical climate indicators and environmental variables.
Data Science Architects: James O'Connor and Aryaa Subedi, serving as Data Science Research Assistants from OWU's data analytics program. They engineered the predictive pipelines and trained the machine learning algorithms.
Utility Partners & Stakeholders: Fred Milan and Frances Wiggins, serving as our primary points of contact at Del-Co Water, providing operational guardrails and industry expertise.