Enhanced nonlinearity interval mapping scheme for high-performance simulation-optimization of watershed-scale BMP placement

Water Resources Research

Volumn 51, Issue 3, 2015, Pages 1831-1845

  Zou, Rui ^a   Riverson, John ^b   Liu, Yong ^c   Murphy, Ryan ^b   Sim, Youn ^d  

^a TETRA TECH INC   (United States)

^b Paradigm Environmental Inc   (United States)

^c PEKING UNIVERSITY   (China)

^d Los Angeles County Flood Control District ^*  (United States)

Author keywords

best management practice; genetic algorithms; interval response coefficient; nonlinearity interval mapping scheme; placement; simulation optimization

Indexed keywords

ALGORITHMS; C++ (PROGRAMMING LANGUAGE); COST EFFECTIVENESS; COSTS; DECISION MAKING; FLOOD CONTROL; GENETIC ALGORITHMS; LARGE SCALE SYSTEMS; LOADING; MAPPING; OPTIMAL SYSTEMS; OPTIMIZATION; WATERSHEDS;

BEST MANAGEMENT PRACTICES; INTERVAL MAPPING; PLACEMENT; RESPONSE COEFFICIENT; SIMULATION OPTIMIZATION;

COMPUTER SOFTWARE;

BEST MANAGEMENT PRACTICE; GENETIC ALGORITHM; NONLINEARITY; OPTIMIZATION; STREAMFLOW; WATER MANAGEMENT; WATERSHED;

CALIFORNIA; LOS ANGELES COUNTY; UNITED STATES;

EID: 85027938469     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/2014WR015772     Document Type: Article

References (44)

1. Ayvaz, M. T., and, H. Karahan, (2008), A simulation/optimization model for the identification of unknown groundwater well locations and pumping rates, J. Hydrol., 357, 76-92.
2. Belaineh, G., R. C. Peralta, and, T. C. Hughes, (1999), Simulation/optimization modeling for water resources management, J. Water Resour. Plann. Manage., 125 (3), 154-161.
3. Burn, D. H., and, J. S. Yulianti, (2001), Waste-load allocation using genetic algorithms, J. Water Resour. Plann. Manage., 127 (2), 121-129.
4. Carmichael, J. J., and, K. M. Strzepek, (2000), A multiple-organic-pollutant simulation/optimization model of industrial and municipal wastewater loading to a riverine environment, Water Resour. Res., 36 (5), 1325-1332.
5. Chiang, L.-C., C. Indrajeet, M. Chetan, and, H. Tao, (2014), Comparing the selection and placement of best management practices in improving water quality using a multiobjective optimization and targeting method, Int. J. Environ. Res. Public Health, 11, 2992-3014.
6. Chipperfield, A., (1997), Introduction to genetic algorithms, Genetic Algorithms in Engineering Systems, edited by, A. M. S. Zalzala, and, P. J. Fleming, 1-41, The Inst. of Electr. Eng., N. Y.
7. Conley, D. J., H. W. Paerl, R. W. Howarth, D. F. Boesch, S. P. Seitzinger, K. E. Havens, C. Lancelot, and, G. E. Likens, (2009), Controlling eutrophication: Nitrogen and phosphorus, Science, 323, 1014-1015.
8. Damodaram, C., and, Zechman, E., (2013), Simulation-optimization approach to design low impact development for managing peak flow alterations in urbanizing watersheds, J. Water Resour. Plann. Manage., 139 (3), 290-298.
9. DePinto, J. V., P. L. Freedman, D. M. Dilks, and, W. M. Larson, (2004), Models quantify the total maximum daily load process, J. Environ. Eng., 130 (6), 703-713.
10. Dhar, A., and, B. Datta, (2008), Optimal operation of reservoirs for downstream water quality control using linked simulation optimization, Hydrol. Processes, 22 (6), 842-853.
11. Elmihoub, T., A. A. Hopgood, L. Nolle, and, A. Battersby, (2006), Hybrid genetic algorithms-A review, Eng. Lett., 13, 124-137.
12. Faulkner, B. R., (2008), Bayesian modeling of the assimilative capacity component of nutrient total maximum daily loads, Water Resour. Res., 44, W08415, doi: 10.1029/2007WR006638.
13. Gen, M., and, R. W. Cheng, (2000), Genetic Algorithm & Engineering Optimization, John Wiley, N. Y.
14. Goldberg, D. E., (1989), Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Mass.
15. He, L., G. H. Huang, and, H. W. Lu, (2008), Health-risk-based groundwater remediation system optimization through clusterwise linear regression, Environ. Sci. Technol., 42 (24), 9237-9243.
16. Hsieh, P. H., J. T. Kuo, E. M. Y. Wu, S. K. Ciou, and, W. C. Liu, (2010), Optimal best management practice placement strategies for nonpoint source pollution management in the Fei-Tsui Reservoir Watershed, Environ. Eng. Sci., 27 (6), 441-449.
17. Huang, Y. F., J. B. Li, G. H. Huang, A. Chakma, and, X. S. Qin, (2003), Integrated simulation-optimization approach for real-time dynamic modeling and process control of surfactant-enhanced remediation at petroleum-contaminated sites, Pract. Periodical Hazard. Toxic Radioact. Waste Manage., 7 (2), 95-105.
18. Ines, A. V. M., K. Honda, A. D. Gupta, P. Droogers, and, R. S. Clemente, (2006), Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture, Agric. Water Manage., 83, 221-232.
19. Johnson, V. M., and, L. L. Rogers, (2000), Accuracy of neural network approximations in simulation-optimization, J. Water Resour. Plann. Manage., 126 (2), 48-56.
20. Kaini, P., K. Artita, and, J. W. Nicklow, (2012), Optimizing structural best management practices using SWAT and genetic algorithm to improve water quality goals, Water Resour. Manage., 26 (7), 1827-1845.
21. Karatzas, G. P., and, G. F. Pinder, (1993), Groundwater management using numerical simulation and the outer approximation method for global optimization, Water Resour. Res., 29 (10), 3371-3378.
22. Kerachian, R., and, M. Karamouz, (2007), A stochastic conflict resolution model for water quality management in reservoir-river systems, Adv. Water Resour., 30, 866-882.
23. Kramer, D. B., S. Polasky, and, A. Starfield, (2006), A comparison of alternative strategies for cost-effective water quality management in lakes, Environ. Manage., 38 (3), 411-425.
24. Lee, J. G., A. Selvakumar, K. Alvi, J. Riverson, J. X. Zhen, L. Shoemaker, F. H. Lai, (2012), A watershed-scale design optimization model for stormwater best management practices, Environ. Modell. Software, 37, 6-18.
25. Limbrunner, J., R. Vogel, S. Chapra, and, P. Kirshen, (2013), Classic optimization techniques applied to stormwater and nonpoint source pollution management at the watershed scale, J. Water Resour. Plann. Manage., 139 (5), 486-491.
26. Luo, B., and, J. You, (2007), A watershed-simulation and hybrid optimization modeling approach for water-quality trading in soil erosion control, Adv. Water Resour., 30 (9), 1902-1913.
27. Maringanti, C., I. Chaubey, and, J. Popp, (2009), Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollution control, Water Resour. Res., 45, W06406, doi: 10.1029/2008WR007094.
28. Maringanti, C., I. Chaubey, M. Arabi, and, B. Engel, (2011), Application of a multi-objective optimization method to provide least cost alternatives for NPS pollution control, Environ. Manage., 48, 448-461.
29. Mujumdar, P. P., and, V. V. R. Subbarao, (2004), Fuzzy waste load allocation model: Simulation-optimization approach, J. Comput. Civ. Eng., 18 (2), 120-131.
30. Mulligan, A. E., and, L. C. Brown, (1998), Genetic algorithms for calibrating water quality models, J. Environ. Eng., 124 (3), 202-211.
31. National Research Council (2001), Assessing the TMDL Approach to Water Quality Management, Natl. Acad. Press, Washington, D. C.
32. Park, D., and, L. A. Roesner, (2012), Evaluation of pollutant loads from stormwater BMPs to receiving water using load frequency curves with uncertainty analysis, Water Res., 46, 6881-6890.
33. Qin, X. S., G. H. Huang, G. M. Zeng, and, A. Chakma, (2008), Simulation-based optimization of dual-phase vacuum extraction to remove nonaqueous phase liquids in subsurface, Water Resour. Res., 44, W04422, doi: 10.1029/2006WR005496.
34. Rodriguez, H. G., J. Popp, C. Maringanti, and, I. Chaubey, (2011), Selection and placement of best management practices used to reduce water quality degradation in Lincoln Lake watershed, Water Resour. Res., 47, W01507, doi: 10.1029/2009WR008549.
35. Saadatpour, M., and, A. Afshar, (2007), Waste load allocation modeling with fuzzy goals: Simulation-optimization approach, Water Resour. Manage., 21 (7), 1207-1224.
36. Sechi, G. M., and, A. Sulis, (2009), Water system management through a mixed optimization-simulation approach, J. Water Resour. Plann. Manage., 135 (3), 160-170.
37. Srivastava, P., J. M. Hamlett, P. D. Robillard, and, R. L. Day, (2002), Watershed optimization of best management practices using AnnAGNPS and a genetic algorithm, Water Resour. Res., 38 (3), 1021, doi: 10.1029/2001WR000365.
38. Tetra Tech (2009), Pilot Application of the BMP Decision Support System in the County of Los Angeles, U. S. Environ. Protect. Agency and the County of Los Angeles Dep. of Public Works, Va.
39. USEPA (1999), Draft Guidance for Water Quality-Based Decisions: The TMDL Process, EPA Rep. 841-D-99-001, 2nd ed, United States Environmental Protection Agency, Office of Water, Washington, D. C.
40. Vasquez, J. A., H. R. Maier, B. J. Lence, B. A. Tolson, and, R. O. Foschi, (2000), Achieving water quality system reliability using genetic algorithms, J. Environ. Eng., 126, 954-962.
41. Veith, T. L., M. L. Wolfe, and, C. D. Heatwole, (2003), Optimization procedure for cost effective BMP placement at a watershed scale, J. Am. Water Resour. Assoc., 39 (6), 1331-1343.
42. Yeomans, J. S., (2002), Automatic generation of efficient policy alternatives via simulation-optimization, J. Oper. Res. Soc., 53, 1256-1267.
43. Zou, R., W. S. Lung, and, J. Wu, (2007), An adaptive neural network embedded genetic algorithm approach for inverse water quality modeling, Water Resour. Res., 43, W08427, doi: 10.1029/2006WR005158.
44. Zou, R., Y. Liu, J. Riverson, A. Parker, and, S. Carter, (2010), A nonlinearity interval mapping scheme for efficient waste load allocation simulation-optimization analysis, Water Resour. Res., 46, W08530, doi: 10.1029/2009WR008753.