LAUSR

Best management practices (BMPs) are widely used to reduce nonpoint source pollutions. In order to obtain cost-effective BMPs configurations, optimization methods are introduced. Recent studies show that knowledge on BMP placement can be used to improve existing algorithms for BMPs optimization. However, some important knowledge has not been fully utilized yet, one of which is about the spatial topology among fields and BMPs interactions. In this paper, a new method for BMPs optimization was proposed, which incorporated knowledge of BMPs interactions into a multi-objective genetic algorithm (i.e., ε-NSGAII) based on spatial topology among fields. Then this method was applied to the BMPs optimization in a small agricultural watershed in Southern Manitoba of Canada, and the performance was compared with those of conventional method. In order to make a comprehensive comparison, experiments were conducted under different population sizes (i.e., 60, 100, and 200) and different numbers of fields (i.e., 29, 52, and 79). The results showed that the proposed method was superior to conventional method on the aspect of greater sediment reductions (2% - 17%) at the same cost, and the Pareto curves obtained by the proposed method were more complete. This study demonstrated that incorporating spatial topology among fields into BMPs optimization can lead to better results and this finding could provide valuable references to similar studies.