Open AccessOptimization of the spatial pattern of land use in mountain towns -- a case study of Yuexi county, Anqing city, Anhui province
Author(s) -
Mengyang Wang,
Dong Zeng,
Xin Wei,
Qiaomei Xiang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/569/1/012085
Subject(s) - particle swarm optimization , geography , hectare , china , land use , fragmentation (computing) , computer science , agricultural engineering , civil engineering , algorithm , engineering , agriculture , archaeology , operating system
There are a lot of unused land and land type distribution problems in the land use layout of mountain towns. Mountainous area is widely distributed in China. In the past, scholars focused on the big cities in the planning of land use layout, but paid little attention to the towns in mountainous area.This paper takes Yuexi county, Anqing city, Anhui province as the research area, and based on the principle of multi-objective particle swarm optimization. Firstly, the current land use map was pretreated with unused land and aggregation. The unused land was converted into a high-grade land type according to the suitability map. The aggregation processing increases the aggregation of a certain land type in the eight neighboring areas. Then, the multi-objective function is combined with the particle swarm optimization(PSO) algorithm, and the PSO algorithm is used to update the iterative operation, and the optimal layout scheme is selected. Finally, from the area changes before and after the optimization, economic, ecological benefits and spatial layout of the evaluation. The results showed that the optimized economic and ecological benefits were 9092.7397 million yuan and 40.09377 million yuan higher than the original, respectively. Moreover, after the optimization, the landscape fragmentation index of the original layout was improved by 0.019930486 per hectare compared with that of the original, and the aggregation was greatly improved in space, so the research has corresponding value.