Integrating genetic algorithm with AnnAGNPS for optimizing BMPs placement to reduce sheet/rill and ephemeral gully erosion

In order to effectively reduce nonpoint source pollutants in agricultural areas within a watershed, a combination of Best Management Practices (BMPs) is selected based on their economic and environmental effectiveness. However, determining the optimal combination can be challenging due to the implementation costs and the consideration of decision makers' preferences. This research presents a methodology for integrating a genetic algorithm with the Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) to effectively select the most efficient BMPs placement for a given watershed. By optimizing BMPs placement, the model can minimize sediment loads from different types of erosion, including sheet/rill, ephemeral gully, and total erosion at the minimal cost. Results demonstrated that BMP placement by the optimization model reduced sediment load caused by sheet/rill by 84.6 %, ephemeral gully by 85.4 %, and total erosion by 86.3 % in the study watershed. Additionally, the model achieved these results at a minimal cost, making it a cost-effective solution for sediment load reduction in the watershed. Also, the results showed the effective implementation of the developed optimization approach for strategically locating BMPs in specific areas, rather than implementing them throughout the entire watershed. By targeting these areas and implementing suitable BMPs, the model was able to reduce the amount of sediment load and remain cost-effective. The proposed weighted overlay technique helped to place BMPs within agricultural fields instead of AnnAGNPS cells, making it easier for farmers to adopt and effectively reduce sediment load in each field. The developed model in the current study can be applied by decision makers in other watersheds with limited resources for implementing BMPs.