Surrogate modelling-based multi-objective optimization for best management practices of nonpoint source pollution.

The integrated application of hydrological models and best management practices (BMPs) serves as a pivotal decision-making tool for managing nonpoint source (NPS) pollution in watersheds. Optimizing and selecting BMP options are critical challenges in managing NPS pollution, as these processes are typically computationally expensive and involve mixed discrete-continuous decision variables. Our study integrated a novel method, the multi-objective adaptive surrogate modeling-based optimization for constrained hybrid problems (MO-ASMOCH), with the distributed Soil and Water Assessment Tool (SWAT) model to efficiently optimize the deployment of BMPs in the Four Lakes watershed of China.