The purpose of this paper is to discuss the land utilization decision making based on WNN model in the three needs of agricultural production, construction and development, and ecological protection, which can provide references for sustainable use of land resources and ensuring the coordinated development with economic benefits, social benefits and ecological benefits. Method of wavelet neural network (WNN) model was employed. The results indicate that the WNN model of land utilization was built by using the data of the Second National Land Survey in Yuzhong basin, which has obtained different types of land utilization in the three needs: 1) in agricultural production needs, Jinya is in a semi-intensive used state, Chengguan, Xiaguanying, Dingyuan and Xiaogangying are in a semi-extensive used state, and Heping and Lianda are in an extensive used state. The land input-output ratio in the agricultural production needs is uncoordinated. 2) Land utilization type for the construction and development needs in Yuzhong basin is in an extensive transition used state. Jinya is in a semi-intensive used state, Heping, Dingyuan and Lianda are in a semi-extensive used state, Chengguan and Xiaguanying are in an extensive used state. A good proportion of the land input-output in the construction and development needs hasn’t been achieved. 3) In ecological protection needs, land utilization in Yuzhong basin is in a semi-extensive and extensive used state. Heping, Dingyuan, Lianda and Jinya are in a semi-extensive used state, meanwhile, Chengguan and Xiaguanying are in an extensive transition used state. The conclusions are as follows: 1)the needs of different conditions and efficiency must be considered in the study of land utilization decision making. And then, decision-making analysis is made for different land use types for achieving the optimal allocation and sustainable use of land resources, improving land use and management level, and ensuring the coordinated development with economic benefits, social benefits and ecological benefits; 2) WNN model is more scientific and operational to the study of land utilization decision making.
Key words
wavelet neural network(WNN)model /
land use efficiency index /
agricultural economy /
land utilization decision /
land use intensity index
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References
[1] 顾湘. 区域产业结构调整与土地集约利用研究[D]. 南京: 南京农业大学, 2007. [GU Xiang. Study on Regional Industrial Structure Adjustment and Land Intensive Use. Nanjing: Nanjing Agricultural University, 2007.]
[2] 王葆芳, 贾宝全, 杨晓晖, 等. 干旱区土地利用方式对沙漠化土地恢复能力的评价[J]. 生态学报, 2002, 22(12): 2030-2035. [WANG Bao-fang, JIA Bao-quan, YANG Xiao-hui, et al. Effects of land-use types on restoration of desertified land in arid zone of China. Acta Ecologica Sinica, 2002, 22(12): 2030-2035.]
[3] 聂艳, 于婧, 胡静, 等. 基于系统协调度的武汉城市土地集约利用评价[J]. 资源科学, 2009, 31(11): 1934-1939. [NIE Yan, YU Jing, HU Jing, et al. An evaluation on intensive use of urban land in Wuhan City based on a system coordination degree model. Resources Science, 2009, 31(11): 1934-1939.]
[4] 何为, 修春亮. 吉林省城市土地集约利用的空间分异[J]. 自然资源学报, 2011, 26(8): 1287-1296. [HE Wei, XIU Chun-liang. Analysis of spatial difference in regional urban land intensive utilization: A case study of Jilin Province. Journal of Natural Resources, 2011, 26(8): 1287-1296.]
[5] 杨海龙, 吕耀, 焦雯珺, 等. 传统农业地区土地利用方式变化的驱动因子分析——基于贵州省从江县农户行为的实证研究[J]. 资源科学, 2010, 32(6): 1050-1056. [YANG Hai-long, Lü Yao, JIAO Wen-jun, et al. Analysis on driving factors of land use change in traditional agricultural regions: An empirical study based on household behaviors. Resources Science, 2010, 32(6): 1050-1056.]
[6] Pijanowski B C, Brown D G, Shellito B A, et al. Using neural networks and GIS to forecast land use changes: A land transformation model [J]. Computers, Environment and Urban Systems, 2002, 26: 553-575.
[7] 常青, 王仰麟, 吴健生, 等. 城市土地集约利用程度的人工神经网络判定——以深圳市为例[J]. 中国土地科学, 2007, 21(4): 26-31. [CHANG Qing, WANG Yang-lin, WU Jian-sheng, et al. Urban land use intensity assessment based on artificial neural networks: A case of Shenzhen. China Land Science, 2007, 21(4): 26-31.]
[8] 贺三维, 潘鹏, 诸云强, 等. 农用地集约利用评价的新模型研究[J]. 自然资源学报, 2012, 27(3): 460-467. [HE San-wei, PAN Peng, ZHU Yun-qiang, et al. The application of a new model in intensive use evaluation of agricultural land. Journal of Natural Resources, 2012, 27(3): 460-467.]
[9] 彭毅. 主体功能区战略引导下的区域土地利用模式及情景模拟[D]. 浙江: 浙江大学, 2009. [PENG Yi. Regional Land Use Pattern and Seenario Simulation Guided by Development Priority Zoning: A Case Study of Suichang County in Zheiiang Province. Hangzhou: Zhejiang University, 2009.]
[10] 张德丰, 等. MATLAB神经网络应用设计[M]. 北京: 机械工业出版社, 2009. [ZHANG De-feng, et al. The Application Design of MATLAB Neural Network. Beijing: China Machine Press, 2009.]
[11] 陈振伟, 郭拯危. 小波神经网络预测模型的仿真实现[J]. 计算机仿真, 2008, 25(6): 147-150. [CHEN Zhen-wei, GUO Zheng-wei. Simulation realization of prediction model based on wavelet neural network. Computer Simulation, 2008, 25(6): 147-150.]
[12] 高克异, 许红卫, 王珂, 等. 基于BP神经网络的低丘红壤资源开发利用评价研究[J]. 科技通报, 2010, 26(5): 385-390. [GAO Ke-yi, XU Hong-wei, WANG Ke, et al. Study on investigation and evaluation of hilly red resources in Zhejiang Province by BP neural network. Bulletin of Science And Technology, 2010, 26(5): 385-390.]
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