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Optimal spatial land-use allocation for limited development ecological zones based on the geographic information system and a genetic ant colony algorithm

机译:基于地理信息系统和遗传蚁群算法的有限开发生态区空间土地利用优化分配

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摘要

Limited development ecological zones (LDEZs) are often located in poverty-stricken, ecologically vulnerable areas where ethnic minorities reside. Studies on optimal spatial land-use allocation in LDEZs can promote economic and intensive land use, improve soil quality, facilitate local socioeconomic development, and maintain environmental stability. In this study, we optimized spatial land-use allocations in an LDEZ using the geographic information system (GIS) and a genetic ant colony algorithm (GACA). The multi-objective function considers economic benefits and ecological green equivalents, and improves soil erosion. We developed the GACA by integrating a genetic algorithm (GA) with an ant colony algorithm (ACA). This avoids a large number of redundant iterations and the low efficiency of the GA, and the slow convergence speed of the ACA. The study area is located in Pengyang County, Ningxia, China, which is a typical LDEZ. The land-use data were interpreted from remote sensing (RS) images and GIS. We determined the optimal spatial land-use allocations in the LDEZ using the GACA in the GIS environment. We compared the original and optimal spatial schemes in terms of economic benefits, ecological green equivalents, and soil erosion. The results of the GACA were superior to the original allocation, the ACA, and the multi-objective genetic algorithm, in terms of the optimum, time, and robust performance indexes. We also present some suggestions for the reasonable development and protection of LDEZs.
机译:有限发展生态区(LDEZ)通常位于少数民族居住的贫困且生态脆弱的地区。对最不发达区域的最佳空间土地利用分配进行研究可促进经济和集约土地利用,改善土壤质量,促进当地社会经济发展并维持环境稳定。在这项研究中,我们使用地理信息系统(GIS)和遗传蚁群算法(GACA)优化了LDEZ中的空间土地利用分配。多目标函数考虑了经济效益和生态绿色当量,并改善了土壤侵蚀。我们通过将遗传算法(GA)与蚁群算法(ACA)集成来开发GACA。这样可以避免大量的重复迭代和GA的低效率,以及ACA的收敛速度较慢。研究区域位于典型的LDEZ——中国宁夏的彭阳县。从遥感(RS)图像和GIS解释了土地利用数据。我们在GIS环境中使用GACA确定了LDEZ中的最佳空间土地利用分配。我们从经济利益,生态绿色当量和土壤侵蚀方面比较了原始和最佳空间方案。 GACA的结果在最佳,时间和鲁棒性能指标方面均优于原始分配,ACA和多目标遗传算法。我们还为合理开发和保护LDEZ提供了一些建议。

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