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.
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.
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 ?…
The authors have nothing to disclose.
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-).