Limits...
Comparing the selection and placement of best management practices in improving water quality using a multiobjective optimization and targeting method.

Chiang LC, Chaubey I, Maringanti C, Huang T - Int J Environ Res Public Health (2014)

Bottom Line: Suites of Best Management Practices (BMPs) are usually selected to be economically and environmentally efficient in reducing nonpoint source (NPS) pollutants from agricultural areas in a watershed.The results showed that the optimization is less effective when vegetated filter strips (VFS) are not considered, and it requires much longer computation times than the targeting method to search for optimum BMPs.Although the targeting method is effective in selecting and placing an optimum BMP, larger areas are needed for BMP implementation to achieve the same pollutant reductions as the optimization method.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil and Disaster Prevention Engineering, National United University, Miaoli 36003, Taiwan. lchiang@nuu.edu.tw.

ABSTRACT
Suites of Best Management Practices (BMPs) are usually selected to be economically and environmentally efficient in reducing nonpoint source (NPS) pollutants from agricultural areas in a watershed. The objective of this research was to compare the selection and placement of BMPs in a pasture-dominated watershed using multiobjective optimization and targeting methods. Two objective functions were used in the optimization process, which minimize pollutant losses and the BMP placement areas. The optimization tool was an integration of a multi-objective genetic algorithm (GA) and a watershed model (Soil and Water Assessment Tool-SWAT). For the targeting method, an optimum BMP option was implemented in critical areas in the watershed that contribute the greatest pollutant losses. A total of 171 BMP combinations, which consist of grazing management, vegetated filter strips (VFS), and poultry litter applications were considered. The results showed that the optimization is less effective when vegetated filter strips (VFS) are not considered, and it requires much longer computation times than the targeting method to search for optimum BMPs. Although the targeting method is effective in selecting and placing an optimum BMP, larger areas are needed for BMP implementation to achieve the same pollutant reductions as the optimization method.

Show MeSH
Pareto-optimal fronts for the sensitivity analysis of genetic algorithm (GA) parameters for the optimization model with a set of 171 BMP options (All).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3987017&req=5

ijerph-11-02992-f003: Pareto-optimal fronts for the sensitivity analysis of genetic algorithm (GA) parameters for the optimization model with a set of 171 BMP options (All).

Mentions: The optimal GA parameters were selected using the sensitivity analysis of the optimal front. A total of seven sets of sensitivity analyses were performed for 171 BMPs. The sensitivity of GA parameters for the optimization model with a set of 171 BMP options (All), namely, population size, number of generations, crossover probability, and mutation probability, are shown in Table 1. Two GA parameters, population size and number of generations, can influence the computing time of the optimization. For example, when the population size increased from 100 to 5,000, the computation time increased from 10 min to 12 h for 1,000 generations. Similarly, the computation time increased from 10 min to 1 h when number of generation increased from 100 to 5,000. The maximum population size tested in this study was 5,000, with which the GA optimization tool could result in the most spread in the solution space in terms of the percentage of BMP-implemented pasture area for all different sets of BMP options. The sensitivity analysis results of the VFS42 BMP options (VFS42) and 171 BMP options (All) were selected for comparison (Figure 3 and Figure 4). It is because buffer strips are the most effective BMP in reducing pollutant losses and considerably greater pollutant reduction is expected if buffer strips with a VFS ratio of 42 are considered in the suite of BMP options.


Comparing the selection and placement of best management practices in improving water quality using a multiobjective optimization and targeting method.

Chiang LC, Chaubey I, Maringanti C, Huang T - Int J Environ Res Public Health (2014)

Pareto-optimal fronts for the sensitivity analysis of genetic algorithm (GA) parameters for the optimization model with a set of 171 BMP options (All).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3987017&req=5

ijerph-11-02992-f003: Pareto-optimal fronts for the sensitivity analysis of genetic algorithm (GA) parameters for the optimization model with a set of 171 BMP options (All).
Mentions: The optimal GA parameters were selected using the sensitivity analysis of the optimal front. A total of seven sets of sensitivity analyses were performed for 171 BMPs. The sensitivity of GA parameters for the optimization model with a set of 171 BMP options (All), namely, population size, number of generations, crossover probability, and mutation probability, are shown in Table 1. Two GA parameters, population size and number of generations, can influence the computing time of the optimization. For example, when the population size increased from 100 to 5,000, the computation time increased from 10 min to 12 h for 1,000 generations. Similarly, the computation time increased from 10 min to 1 h when number of generation increased from 100 to 5,000. The maximum population size tested in this study was 5,000, with which the GA optimization tool could result in the most spread in the solution space in terms of the percentage of BMP-implemented pasture area for all different sets of BMP options. The sensitivity analysis results of the VFS42 BMP options (VFS42) and 171 BMP options (All) were selected for comparison (Figure 3 and Figure 4). It is because buffer strips are the most effective BMP in reducing pollutant losses and considerably greater pollutant reduction is expected if buffer strips with a VFS ratio of 42 are considered in the suite of BMP options.

Bottom Line: Suites of Best Management Practices (BMPs) are usually selected to be economically and environmentally efficient in reducing nonpoint source (NPS) pollutants from agricultural areas in a watershed.The results showed that the optimization is less effective when vegetated filter strips (VFS) are not considered, and it requires much longer computation times than the targeting method to search for optimum BMPs.Although the targeting method is effective in selecting and placing an optimum BMP, larger areas are needed for BMP implementation to achieve the same pollutant reductions as the optimization method.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil and Disaster Prevention Engineering, National United University, Miaoli 36003, Taiwan. lchiang@nuu.edu.tw.

ABSTRACT
Suites of Best Management Practices (BMPs) are usually selected to be economically and environmentally efficient in reducing nonpoint source (NPS) pollutants from agricultural areas in a watershed. The objective of this research was to compare the selection and placement of BMPs in a pasture-dominated watershed using multiobjective optimization and targeting methods. Two objective functions were used in the optimization process, which minimize pollutant losses and the BMP placement areas. The optimization tool was an integration of a multi-objective genetic algorithm (GA) and a watershed model (Soil and Water Assessment Tool-SWAT). For the targeting method, an optimum BMP option was implemented in critical areas in the watershed that contribute the greatest pollutant losses. A total of 171 BMP combinations, which consist of grazing management, vegetated filter strips (VFS), and poultry litter applications were considered. The results showed that the optimization is less effective when vegetated filter strips (VFS) are not considered, and it requires much longer computation times than the targeting method to search for optimum BMPs. Although the targeting method is effective in selecting and placing an optimum BMP, larger areas are needed for BMP implementation to achieve the same pollutant reductions as the optimization method.

Show MeSH