- Land and Water Conservation Engineering
- Development of Artificial Neural Networks (ANN) for forecasting watersheds' hydrologic behaviors
- Development of Decision Support System (DSS) for human impact evaluation on ecosystem sustainability
- Evaluation and development of watershed/water quality models
- Description, analysis and prevention of non-point source pollution at laboratory, field, watershed and regional scales
Developing Decision Support System for Analyzing Sedimentation Reduction Strategies for Tuttle Creek Lake
This project is aimed at enhancing the understanding of how water interacts with upland soils, slopes and land-management practices to impact water quantity and quality yields to streams and ultimately to reservoirs, and how these yields interact with unique combinations of lithology, soils, vegetation, micro-climate, and land toward achieving an environmentally sustainable and economically efficient system. The study helps to apply Decision Support System (DSS) technology to develop a Best Management Practice (BMPs) allocation plan for sediment control.
Developing Nutrient, Sediment, Flow and Temperature Estimates for Fish Community Condition Prediction Across the Agricultural Regions of Michigan and Wisconsin
The goal of this project is to use spatially explicit models to generate a suite of water quality variables that can be related to stream fish survey data across Michigan and Wisconsin.
Water Quantity and Water Quality Effects of BMPs Implementation in Urban Areas
The goal of this study is to conduct a comprehensive literature review and synthesize this information in a way that can easily assist stakeholders and policy makers in the decision-making process regarding Low Impact Development (LID) implementation strategy. This database will provide the range of effectiveness for various BMPs as regard to site-specific factors.