QQ客服 官方微博 反馈留言

	Optimal spatial allocation of water resources based on Pareto ant colony algorithm
	Hou J. W. ; Mi W. B. ; Sun J. L.
	2014
关键词	optimal allocation water resources remote sense geographical information system Pareto ant colony algorithm land-use allocation programming approach mating optimization reservoir operation management models genetic algorithm hbmo algorithm large areas systems gis
英文摘要	The spatial allocation of water resources is optimised using the multi-objective functions and multi-constrained conditions of the Pareto ant colony algorithm (PACA). The objective function is the highest benefit to the economy, society and the environment, while the constraints include water supply, demand and quality. The PACA is improved by limiting local pheromone scope and dynamically updating global pheromone levels. Since both strategies guide the ant towards borders of high-pheromone concentration, the new approach enhances the global search capability and convergence speed. Programming, database management and interface tools are then integrated into geographic information systems (GIS) software. The study area is located in Zhenping County, Henan Province, China, and water resource data are obtained using remote sensing (RS) and GIS technology. The improved PACA is solved in the GIS environment. Optimal spatial allocation schemes are obtained for surface, ground and transferred water and the model yields optimal spatial benefit schemes of water resources, embracing economic, social and ecological benefits. The results of improved PACA are superior to those of other intelligent optimisation algorithms, including the ant colony algorithm, multi-objective genetic algorithm and back-propagation artificial neural network. Therefore, the integration of RS, GIS and PACA can effectively optimise the large-scale, multi-objective allocation of water resources. The model also enhances the global search capability, convergence speed and result precision, and can potentially solve other optimal spatial problems with multi-objective functions.
出处	International Journal of Geographical Information Science
卷	28
期	2
页	213-233
收录类别	SCI
语种	英语
ISSN号	1365-8816
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/29839]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Hou J. W.,Mi W. B.,Sun J. L.. Optimal spatial allocation of water resources based on Pareto ant colony algorithm. 2014.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。