Statewide Prioritization of Stormwater Infrastructure for Climate-Resilient Inspection and Maintenance
Overview
Developed a statewide geospatial decision-support framework for prioritizing inspection and maintenance of highway stormwater infrastructure during a research internship with the Maryland Department of Transportation – State Highway Administration (MDOT SHA).
The project addressed a major operational challenge faced by transportation agencies: allocating limited inspection and maintenance resources across thousands of drainage assets while accounting for increasing flood, erosion, and environmental risks associated with climate change.
The resulting system integrates spatial analytics, infrastructure condition data, and environmental indicators to support proactive maintenance planning.
Technical Architecture
The framework combines geospatial data integration with risk-based prioritization algorithms.
Geospatial Data Integration
A comprehensive spatial infrastructure database was constructed by integrating multiple datasets, including:
- Hydrology, watershed, and floodplain GIS layers
- Topography and terrain characteristics
- Soil and land-use information
- Environmental impairment and watershed condition data
- Infrastructure attributes and historical maintenance records
- Climate and extreme rainfall indicators
Spatial analysis operations such as proximity analysis, watershed delineation, and terrain evaluation were performed using ArcGIS.
Risk Modeling and Prioritization
A multi-criteria risk assessment framework was developed to evaluate inspection priorities across the stormwater network. Risk indicators incorporated:
- Flood exposure potential
- Erosion susceptibility
- Environmental vulnerability
- Infrastructure condition attributes
- Downstream impact and system connectivity
Weighted scoring and prioritization algorithms were implemented in Python to compute asset-level risk indices and generate inspection priority rankings.
Decision-Support Tools
Two operational tools were developed to support agency decision-making:
Inspection Prioritization Tool
- Automated risk scoring for stormwater assets
- Ranking of infrastructure based on inspection priority
- Configurable weighting to reflect policy or operational preferences
Maintenance Planning and Risk Mapping Tool
- Statewide vulnerability and risk heat maps
- Identification of high-risk geographic clusters
- Visualization tools for inspection planning and resource allocation
These tools support preventive maintenance strategies and enable agencies to shift from reactive repairs toward risk-informed infrastructure management.
Empirical Impact
The framework enabled data-driven infrastructure prioritization across a large statewide asset inventory.
Key outcomes include:
- Integration of multiple geospatial datasets into a unified infrastructure database
- Scalable prioritization framework applicable to thousands of drainage assets
- Improved transparency and defensibility of inspection scheduling decisions
- Enhanced ability to identify climate-vulnerable infrastructure locations
- Support for proactive flood and erosion risk mitigation strategies
Engineering Deliverables
- Statewide stormwater infrastructure geospatial database
- Multi-criteria risk assessment and prioritization algorithms
- Python-based scoring and ranking framework
- GIS-based visualization and risk mapping tools
- Decision-support workflows for inspection and maintenance planning
Relevance
Transportation agencies increasingly face the challenge of maintaining aging drainage infrastructure under intensifying climate stressors. Data-driven prioritization tools can significantly improve how limited inspection and maintenance resources are allocated.
This project demonstrates how geospatial analytics and risk modeling can be translated into operational decision-support systems for public infrastructure management.