Leveraging innovization and transfer learning to optimize best management practices in large-scale watershed management

Abstract

Recent research in evolutionary multi-objective optimization (EMO) highlights the concept of “Innovization”, which identifies essential patterns in high-quality, non-dominated solutions. This study introduces a novel method to pinpoint influential Best Management Practices (BMPs) in the Chesapeake Bay Watershed, optimizing the trade-off solution process. This approach, though innovative, demands considerable expertise and involves generating multiple solutions for expert analysis to detect commonly used BMPs. We devised three re-optimization strategies from these findings using an innovized BMP list, efficiently producing high-quality solutions. We also implemented transfer learning to adapt these strategies for new counties, demonstrating effectiveness in four West Virginia counties by reducing decision variables by 3% to 33% and achieving similar reductions in four additional counties. This showcases the potential of combining innovization with transfer learning to simplify complex optimization challenges, emphasizing its significant applicability in real-world settings.