Example Research And Model Of Land Use Structure Optimization

Dynamic and Multi-objective programming method on land use structure optimization is the hot-point on land use planning. With population's growth, economic and social development, Demands for land increase dramatically and the conflict between human and land becomes more and more conspicuous. Land sustainable utilization is an essential aspect to the sustainable development of human society since it acts as a hinge in interrelation among population, resource and environment. Thus, regional sustainable development should aim at strengthening land resource management and optimizing land resources allocation.A model of Dynamic and Multi-objective Programming Method for Land Sustainable Utilization was set by applying dynamic optimum programming theory and adopting multi-objective fuzzy separately. All components in the model were structured based on carefully analyzing land utilization system including time arrays, land use pattern variable, the optimizing goal and the restriction condition. Furthermore, those methods such as natural growth rate, linear regression and grey GM(1,1) were employed to predict parameters relevant to the model. The optimizing results gained from multi-objective genetic algorithm, Pareto solutions, continues to be handled though fuzzy dynamic optimum, seeking optimum planning decision array in combining with the principle of maximum dominance. The thesis mainly includes five chapters as follows:Chapter one is the background, meaning, objectives, content and method of this thesis.Chapter two is the land use structure optimization analysis. Firstly, the paper presents the definition of correlative conception. Then, it analyses the theory basis of land use structure study. Finally, the article introduces the principles and goals of land use structure optimization.Chapter three is the construction of the model of the land use structure optimization. Firstly, it introduces the land use structure programming approach. Secondly, it builds the specific model to solve the problems. Finally, select the best ways to reach the goals.Chapter four is taking Nanjing for example. The Pareto solutions from genetic algorithm can offer more chance for the decision-maker to select. The optimizing result in previous time slice acts as the current restriction (which also called market economy restriction) to conductive to programming in follow-up step, which can guarantee the dynamic and timing attribute of the land optimum result. The application of multi-objective fuzzy dynamic optimum programming method can help avoid the subjectivity and effectively guarantee to get the optimum solution. Only when keeping on exchanging information with local experts and modifying the multi-objective dynamic programming model can the practicability of the model be guaranteed.Chapter five is the result of the study of the instance of Nanjing. It shows that integration of multi-objective genetic algorithm and multi-objective fuzzy optimum dynamic programming in building up the model of multi-objective dynamic programming is of much stronger practicability. The results of the programming not only coordinate the relationship among population, resource and environmental within planned period, but also make the best syntactical benefit.