कृषि संरक्षण एक स्वाट मॉडल का उपयोग आचरण के स्थानिक Multiobjective अनुकूलन और एक विकासवादी एल्गोरिथ्म
Summary
यह काम एक अनुकूलन विकासवादी एल्गोरिदम उपयोग इष्टतम (सबसे कम लागत) पानी की गुणवत्ता में सुधार के लक्ष्यों का एक सेट निर्दिष्ट के लिए कृषि संरक्षण प्रथाओं के प्लेसमेंट के लिए हल करने घटक के साथ एक पानी की गुणवत्ता मॉडल की एक एकीकरण को दर्शाता है. समाधान एक बहु – उद्देश्य दृष्टिकोण का उपयोग कर, tradeoffs की स्पष्ट मात्रा का ठहराव के लिए अनुमति देता है उत्पन्न कर रहे हैं.
Abstract
Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,5,12,20) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization.
Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods3,4,9,10,13-15,17-19,22,23,25. In this application, we demonstrate a program which follows Rabotyagov et al.’s approach and integrates a modern and commonly used SWAT water quality model7 with a multiobjective evolutionary algorithm SPEA226, and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.
Protocol
Discussion
हम एक एकीकृत सिमुलेशन अनुकूलन वाटरशेड मिश्रण सबसे कम लागत और कृषि संरक्षण प्रथाओं के स्थान पर वाटरशेड स्तर के पोषक तत्वों की कमी के उद्देश्यों की एक श्रेणी प्राप्त शामिल विन्यास की Pareto कुशल सेट के लिए ?…
Disclosures
The authors have nothing to disclose.
Acknowledgements
इस शोध लक्षित अमेरिकी पर्यावरण संरक्षण एजेंसी watersheds अनुदान कार्यक्रम (WS97704801 # परियोजना) से प्राप्त समर्थन से हिस्से में वित्त पोषित किया गया था, मिलकर प्राकृतिक और मानव सिस्टम (DEB1010259 कार्ड KLIN # परियोजना) के राष्ट्रीय विज्ञान फाउंडेशन गतिशीलता, और अमेरिका विभाग कृषि – Foodand कृषि समन्वित कृषि परियोजना के राष्ट्रीय संस्थान (# 20116800230190 परियोजना कार्ड-).
References
- American Society of Agricultural and Biological Engineers. Design, layout, construction, and maintenance of terrace systems. ASAE Standard. , S268 (2003).
- . . Raccoon River Watershed water quality master plan. , (2011).
- Arabi, M., Govindaraju, R. S., Hantush, M. M. Cost-effective allocation of watershed management practices using a genetic algorithm. Water Resour. Res. 42, W10429 (2006).
- Bekele, E. G., Nicklow, J. W. Multiobjective management of ecosystem services by integrative watershed modeling and evolutionary algorithms. Water Resour. Res. 41, W10406 (2005).
- Carpentier, C. L., Bosch, D. J., Batie, S. S. Using spatial information to reduce costs of controlling agricultural nonpoint source pollution. Am. J. Agr. Econ. 61, 404-413 (1998).
- Gassman, P. W., Reyes, M., Green, C. H., Arnold, J. G. The Soil and Water Assessment Tool: Historical development, applications, and future directions. Trans. ASABE. 50 (4), 1211-1250 (2007).
- Gassman, P. W., Campbell, T., Secchi, S., Jha, M., Arnold, J. G. The i_SWAT software package: a tool for supporting SWAT watershed applications. , 66-69 .
- Gitau, M. W., Veith, T. L., Gburek, W. J., Jarrett, A. R. Watershed level best management practice selection and placement in the Town Brook watershed, New York. J. Am. Water Resour. Assoc. 42, 1565-1581 (2006).
- Jha, M., Rabotyagov, S., Gassman, P. W. Optimal placement of conservation practices using genetic algorithms with SWAT. Int. Agr. Eng. J. 18 (1-2), 41-50 (2009).
- Jha, M. K., Wolter, C. F., Schilling, K. E., Gassman, P. W. Assessment of total maximum daily load implementation strategies for nitrate impairment of the Raccoon River, Iowa. J. Envir. Qual. 39, 1317-1327 (2010).
- Kanna, M., Yang, W., Farnsworth, R., Onal, H. Cost effective targeting of CREP to improve water quality with endogenous sediment deposition coefficients. Am. J. Agr. Econ. 85, 538-553 (2003).
- Kramer, R. A., McSweeny, W. T., Kerns, W. R., Stravros, R. W. An evaluation of alternative policies for controlling agricultural nonpoint source pollution. Water Resour. Bull. 20, 841-8446 (1984).
- Lant, C. L., Kraft, S. E., Beaulieu, J., Bennett, D., Loftus, T., Nicklow, J. Using GIS-based ecological-economic modeling to evaluate policies affecting agricultural watersheds. Ecol. Econ. 55, 467-484 (2005).
- Muleta, M. K., Nicklow, J. W. Evolutionary algorithms for multiobjective evaluation of watershed management decisions. J. Hydroinform. 4 (2), 83-97 (2002).
- Muleta, M. K., Nicklow, J. W. Decision support for watershed management using evolutionary algorithms. J. Water Resour. Plan. Manag. 131 (1), 35-44 (2005).
- Rabotyagov, S. S., Campbell, T., Jha, M., Gassman, P. W., Arnold, J., Kurkalova, L., Secchi, S., Feng, H., Kling, C. L. Least Cost Control of Agricultural Nutrient Contributions to the Gulf of Mexico Hypoxic Zone. Ecol. Appl. 20, 1542-1555 (2010).
- Rabotyagov, S., Jha, M., Campbell, T. Impact of crop rotations on optimal selection of conservation practices for water quality protection. J. Soil. Water Conserv. 65 (6), 369-380 (2010).
- Rabotyagov, S. S., Jha, M., Campbell, T. D. Nonpoint-source pollution for an Iowa watershed: An application of evolutionary algorithms. Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie. , (2010).
- Rabotyagov, S. S., Jha, M., Campbell, T. Searching for Efficiency: Least Cost Nonpoint Source Pollution Control with Multiple Pollutants, Practices, and Targets. J. Nat. and Environ. Sci. 1, 75-90 (2010).
- Ribaudo, M. O. Consideration of off-site impacts in targeting soil conservation programs. Land Econ. 62, 402-411 (1986).
- Ribaudo, M. O. Targeting the conservation reserve program to maximize water quality benefits. Land Econ. 65, 320-332 (1989).
- Srivastava, P., Hamlett, J. M., Robillard, P. D., Day, R. L. Watershed optimization of best management practices using AnnAGNPS and a genetic algorithm. Water Resour. Res. 38 (3), 1-14 (2002).
- Veith, T. L., Wolfe, M. L., Heatwole, C. D. Development of optimization procedure for cost-effective BMP placement. J. Am. Water Resour. Assoc. 39 (6), 1331-1343 (2003).
- Whittaker, G., Confesor, R., Griffith, S. M., Färe, R., Grosskopf, S., Steiner, J. J., Mueller-Warrant, G. W., Banowetz, G. M. A Hybrid Genetic Algorithm for Multiobjective Problems with Activity Analysis-based Local Search. Eur. J. Operat. Res. , (2007).
- Zitzler, E., Laumanns, M., Thiele, L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. CIMNE. , 95-100 (2002).
