Next-generation techniques for parameter reduction for BMP multiobjective optimization in watershed planning

Best Management Practices (BMPs) reduce pollutants, but cost, efficiency, and site-specific constraints limit implementation in water resources planning. This study optimized BMP selection in West Virginia's Chesapeake Bay watershed, where nutrient pollution, sedimentation, runoff, and urbanization are major challenges. The Chesapeake Assessment Scenario Tool was integrated with a multiobjective optimization algorithm to identify cost-effective BMP strategies. Four BMP groups were evaluated: agricultural, developed, septic, and natural, targeting nitrogen reduction. The optimization involved 205 BMPs and up to 65,260 variables. The variables consist of four key components: land-river segment, agency, load source, and BMP type. Three land-use-based techniques were developed using innovization to enhance optimization efficiency by extracting knowledge from optimization results to reduce variables. The best method achieved a 97 % reduction in variables without compromising solution quality. These findings demonstrate that large, complex watershed optimization problems can now be solved efficiently, enabling more scalable and effective regional water management strategies.