当前位置:主页 > 科技论文 > AI论文 >

城市元胞自动机扩展邻域效应的测量与校准研究

发布时间:2017-01-13 12:04

  本文关键词:基于神经网络的元胞自动机及模拟复杂土地利用系统,由笔耕文化传播整理发布。


[1] 段晓东, 王存睿, 刘向东. 2012.元胞自动机理论研究及其仿真应用. 北京: 科学出版社. [Duan X D, Wang C R, Liu X D. 2012. Cellular automata theory research and simulation applications. Beijing, China: Science Press.]

[2] 柯新利, 邓祥征, 陈勇. 2011. 元胞空间分区及其对GeoCA模型模拟精度的影响. 遥感学报, 15(3): 512-523. [Ke X L, Deng X Z, Chen Y. 2011. A partitioned GeoCA based on dual-constraint spatial cluster and its effect on the accuracy of simulating result. Journal of Remote Sensing, 15(3): 512-523.]

[3] 李乐, 齐伟, 张新花, 等. 2009. 栖霞市土地利用空间格局的邻域关系. 应用生态学报, 20(4): 909-915. [Li L, Qi W, Zhang X H, et al.2009. Neighborhood relationships of land use spatial pattern in Qixia City. Chinese Journal of Applied Ecology, 20(4): 909-915.]

[4] 黎夏, 叶嘉安. 2005. 基于神经网络的元胞自动机及模拟复杂土地利用系统. 地理研究, 24(1): 19-27. [Li X, Yeh A G O. 2007. Cellular automata for simulating complex land use systems using neural networks. Geographical Research, 24(1): 19-27.]

[5] 黎夏, 叶嘉安, 刘小平. 2007. 地理模拟系统: 元胞自动机与多智能体. 北京: 科学出版社. [Li X, Yeh A G O, Liu X P. 2007.Geographical simulation system: cellular automata and multi-agent. Beijing, China: Science Press.]

[6] 厦门市统计局. 2008. 厦门经济特区年鉴: 2008[DB/OL]. 2008-08-01[2014-09-28]. [Xiamen City Statistics Bureau. 2008. Yearbook of Xiamen special economic zone in 2008[DB/OL]. 2008-08-01[2014-09-28]. ]

[7] 厦门市统计局. 2011. 厦门经济特区年鉴: 2011[DB/OL]. 2011-08-01[2014-09-28]. [Xiamen City Statistics Bureau. 2011. Yearbook of Xiamen special economic zone in 2011[DB/OL]. 2011-08-01[2014-09-28]. ]

[8] 杨青生, 黎夏. 2007. 基于遗传算法自动获取CA模型的参数: 以东莞市城市发展模拟为例. 地理研究, 26(2): 229-237. [Yang Q S, Li X. 2007. Calibrating urban cellular automata using genetic algorithms. Geographical Research, 26(2): 229-237.]

[9] Batty M, Xie Y. 1994. From cells to cities. Environment and Planning B, 21(7): 31-48.

[10] Batty M, Xie Y C, Sun Z L. 1999. Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23(3): 205-233.

[11] Chen B Q, Xu H Q. 2005. Urban expansion and its driving force analysis using remote sensed data: a case of Xiamen City. Economic Geography, 25(1): 79-83.

[12] Clarke K C, Hoppen S, Gaydos L. 1997. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24(2): 247-261.

[13] Couclelis H. 1988. Of mice and men: what rodent population scan teach us about complex spatial dynamics. Environment and Planning A, 20: 99-109.

[14] Feng Y, Liu Y. 2013. A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing. International Journal of Geographical Information Science, 27(3): 449-466.

[15] Feng Y J, Liu Y, Tong X H, et al.2011. Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape and Urban Planning, 102(3): 188-196.

[16] Hagoort M, Geertman S, Ottens H. 2008. Spatial externalities, neighbourhood rules and CA land-use modelling. The Annals of Regional Science, 42(1): 39-56.

[17] Hansen H S. 2008. Quantifying and analysing neighbourhood characteristics supporting urban land-use modelling//Bernard L, Christensen A F, Pundt H. The European information society. Berlin, Germany: Springer: 283-299.

[18] Kennedy J, Eberhart R C. 1995. Particle swarm optimization//IEEE Service Center. IEEE international conference on neural networks. Perth, Australia: Piscataway IEEE Press: 1942-1948.

[19] Li X, Yeh A G O. 2000. Modelling sustainable urban development by the integration of constrained cellular automata and GIS. International Journal of Geographical Information Science, 14(2): 131-152.

[20] Liao J F, Tang L N, Shao G F, et al.2014. A neighbor decay cellular automata approach for simulating urban expansion based on particle swarm intelligence. International Journal of Geographical Information Science, 28(4): 720-738.

[21] Liu X P, Li X, Yeh A G O, et al.2007. Discovery of transition rules for geographical cellular automata by using ant colony optimization. Science in China: Series D, 50(10): 1578-1588.

[22] Pan Y, Roth A, Yu Z R, et al.2010. The impact of variation in scale on the behavior of a cellular automata used for land use change modeling. Computers, Environment and Urban Systems, 34(5): 400-408.

[23] Pontius Jr R G, Huffaker D, Denman K. 2004. Useful techniques of validation for spatially explicit land-change models. Ecological Modelling, 179(4): 445-461.

[24] Tobler W R. 1970. A computer movie simulating urban growth in the Detroit Region. Economic Geography, 46: 234-240.

[25] Tobler W R. 1979. Cellular geography//Gale S, Olsson G. Philosophy in geography. Berlin, Germany: Springer: 379-386.

[26] Van Vliet J, Naus N, Van Lammeren R J, et al.2013. Measuring the neighbourhood effect to calibrate land use models. Computers, Environment and Urban Systems, 41: 55-64.

[27] Verburg P H, De Nijs T C M, Van Ritsema Eck J, et al.2004. A method to analyse neighbourhood characteristics of land use patterns. Computers, Environment and Urban Systems, 28(6): 667-690.

[28] White R, Engelen G. 1993. Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environment and Planning A, 25(8): 1175-1199.

[29] Wu F L. 2002. Calibration of stochastic cellular automata: the application to rural-urban land conversions. International Journal of Geographical Information Science, 16(8): 795-818.

[30] Wu F L, Webster C J. 1998. Simulation of land development through the integration of cellular automata and multi-criteria evaluation. Environment and Planning B, 25(1): 103-126.

[31] Yeh A G O, Li X. 2006. Errors and uncertainties in urban cellular automata. Computers, Environment and Urban Systems, 30(1): 10-28.


  本文关键词:基于神经网络的元胞自动机及模拟复杂土地利用系统,,由笔耕文化传播整理发布。



本文编号:237117

资料下载
论文发表

本文链接:https://www.wllwen.com/kejilunwen/rengongzhinen/237117.html


Copyright(c)文论论文网All Rights Reserved | 网站地图 |

版权申明:资料由用户13614***提供,本站仅收录摘要或目录,作者需要删除请E-mail邮箱bigeng88@qq.com