全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于随机森林的元胞自动机城市扩展模拟——以佛山市为例

DOI: 10.18306/dlkxjz.2015.08.001, PP. 937-946

Keywords: 随机森林,元胞自动机,城市扩展,佛山

Full-Text   Cite this paper   Add to My Lib

Abstract:

本文提出一种基于随机森林的元胞自动机城市扩展(RF-CA)模型。通过在多个决策树的生成过程中分别对训练样本集和分裂节点的候选空间变量引入随机因素,提取城市扩展元胞自动机的转换规则。该模型便于并行构建,能在运算量没有显著增加的前提下提高预测的精度,对城市扩展中存在的随机因素有较强的容忍度。RF-CA模型可进行袋外误差估计,以快速获取模型参数;也可度量空间变量重要性,解释各空间变量在城市扩展中的作用。将该模型应用于佛山市1988-2012年的城市扩展模拟中,结果表明,与常用的逻辑回归模型相比,RF-CA模型进行模拟和预测分别能够提高1.7%和2.6%的精度,非常适用于复杂非线性特征的城市系统演变模型与扩展研究;通过对影响佛山市城市扩展的空间变量进行重要性度量,发现对佛山城市扩张模拟研究而言,距国道的距离与距城市中心的距离具有最重要的作用。

References

[1]  1 蔡林霖. 2013. 随机森林的模型选择及其并行化方法[D]. 哈尔滨: 哈尔滨工业大学. [Cai L L. 2013. Model selection of random forest and its parallelization[D]. Harbin, China: Harbin Institute of Technology.]
[2]  2 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[M]. 北京: 科学出版社. [Duan X D, Wang C R, Liu X D. 2012. Cellular automata theory research and simulation applications[M]. Beijing, China: Science Press.]
[3]  3 方匡南, 吴见彬, 朱建平, 等. 2011. 随机森林方法研究综述[J]. 统计与信息论坛, 26(3): 32-38. [Fang K N, Wu J B, Zhu J P, et al.2011. A review of technologies on random forests[J]. Statistics & Information Forum, 26(3): 32-38.]
[4]  4 冯永玖, 刘妙龙, 童小华, 等. 2010. 基于核主成分元胞模型的城市演化重建与预测[J]. 地理学报, 65(6): 665-675. [Feng Y J, Liu M L, Tong X H, et al.2010. Kernel principal components analysis based cellular model for restructuring and predicting urban evolution[J]. Acta Geographica Sinica, 65(6): 665-675.]
[5]  5 冯永玖, 刘艳, 韩震. 2011. 不同样本方案下遗传元胞自动机的土地利用模拟及景观评价[J]. 应用生态学报, 22(4): 957-963. [Feng Y J, Liu Y, Han Z. 2011. Land use simulation and landscape assessment by using genetic algorithm based on cellular automata under different sampling schemes[J]. Chinese Journal of Applied Ecology, 22(4): 957-963.]
[6]  6 何春阳, 史培军, 陈晋, 等. 2005. 基于系统动力学模型和元胞自动机模型的土地利用情景模型研究[J]. 中国科学: 地球科学, 35(5): 464-473. [He C Y, Shi P J, Chen J, et al.2005. Developing land use scenario dynamics model by the integration of system dynamics model and cellular automata model[J]. Science in China: Earth Sciences, 48(11): 1979-1989.]
[7]  7 柯新利, 孟芬, 马才学. 2014. 基于粮食安全与经济发展区域差异的土地资源优化配置: 以武汉城市圈为例[J]. 资源科学, 36(8): 1572-1578. [Ke X L, Meng F, Ma C X. 2014. Optimizing land resource allocation based on food security and regional difference in economic development: a case study in Wuhan metropolitan[J]. Resources Science, 36(8): 1572-1578.]
[8]  8 廖江福, 唐立娜, 王翠平, 等. 2014. 城市元胞自动机扩展邻域效应的测量与校准研究[J]. 地理科学进展, 33(12): 1624-1633. [Liao J F, Tang L N, Wang C P, et al.2014. Measuring and calibrating extended neighborhood effect of urban cellular automata model based on particle swarm optimization[J]. Progress in Geography, 33(12): 1624-1633.]
[9]  9 刘小平, 黎夏, 叶嘉安, 等. 2007. 利用蚁群智能挖掘地理元胞自动机的转换规则[J]. 中国科学: 地球科学, 37(6): 824-834. [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[J]. Science in China : Earth Sciences, 50(10): 1578-1588.]
[10]  10 龙瀛. 2011. 面向空间规划的微观模拟: 数据、模拟与评价[D]. 北京: 清华大学. [Long Y. 2011. Urban microsimulation for spatial plan: data, modelling, and evaluation[D]. Beijing, China: Tsinghua University.]
[11]  11 王云飞, 庞勇, 舒清态. 2013. 基于随机森林算法的橡胶林地上生物量遥感反演研究: 以景洪市为例[J]. 西南林业大学学报, 33(6): 38-45. [Wang Y F, Pang Y, Shu Q T. 2013. Counter-estimation on aboveground biomass of Hevea brasiliensis plantation by remote sensing with random forest algorithm: a case study of Jinghong[J]. Journal of Southwest Forestry University, 33(6): 38-45.]
[12]  12 杨青生. 2008. 地理元胞自动机及空间动态转换规则的获取[J]. 中山大学学报: 自然科学版, 47(4): 122-127. [Yang Q S. 2008. Dynamic transition rules for geographical cellular automata[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 47(4): 122-127.]
[13]  13 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[J]. 地理研究, 26(2): 229-237. [Yang Q S, Li X. 2007. Calibrating urban cellular automata using genetic algorithms[J]. Geographical Research, 26(2): 229-237.]
[14]  14 张鸿辉, 尹长林, 曾永年, 等. 2008. 基于SLEUTH模型的城市增长模拟研究: 以长沙市为例[J]. 遥感技术与应用, 23(6): 618-623. [Zhang H H, Yin C L, Zeng Y N, et al.2008. Study on urban growth simulation based on SLEUTH model: Changsha City as an example[J]. Remote Sensing Technology and Application, 23(6): 618-623.]
[15]  15 张亦汉, 黎夏, 刘小平, 等. 2013. 耦合遥感观测和元胞自动机的城市扩张模拟[J]. 遥感学报, 17(4): 872-886. [Zhang Y H, Li X, Liu X P, et al.2013. Urban expansion simulation by coupling remote sensing observations and cellular automata[J]. Journal of Remote Sensing, 17(4): 872-886.]
[16]  16 Breiman L. 2001 a. Random forests[J]. Machine Learning, 45(1): 5-32.
[17]  17 Breiman L. 2001 b. Statistical modeling: the two cultures[J]. Statistical Science, 16(3): 199-231.
[18]  18 Breiman L, Friedman J H, Stone C J, et al.1984. Classification and regression trees[M]. Boca Raton, FL: CRC press.
[19]  19 Chen Y M, Li X, Wang S J, et al.2013. Simulating urban form and energy consumption in the Pearl River Delta under different development strategies[J]. Annals of the Association of American Geographers, 103(6): 1567-1585.
[20]  20 Genuer R, Poggi J -M, Tuleau-Malot C. 2010. Variable selection using random forests[J]. Pattern Recognition Letters, 31(14): 2225-2236.
[21]  21 Hastie T, Tibshirani R, Friedman J H. 2008. The elements of statistical learning: data mining, inference, and prediction (2nd ed.)[M]. New York, NY: Springer.
[22]  22 Kandaswamy K K, Chou K C, Martinetz T, et al.2011. AFP-Pred: a random forest approach for predicting antifreeze proteins from sequence-derived properties[J]. Journal of Theoretical Biology, 270(1): 56-62.
[23]  23 Li X, Yeh A G O. 2001. Calibration of cellular automata by using neural networks for the simulation of complex urban systems[J]. Environment and Planning A, 33(8): 1445-1462.
[24]  24 Li X, Yeh A G O. 2002. Neural-network-based cellular automata for simulating multiple land use changes using GIS[J]. International Journal of Geographical Information Science, 16(4): 323-343.
[25]  25 Liu X P, Li X, Liu L, et al.2008. A bottom-up approach to discover transition rules of cellular automata using ant intelligence[J]. International Journal of Geographical Information Science, 22(11-12): 1247-1269.
[26]  26 Peters J, De Baets B, Verhoest N E C, et al.2007. Random forests as a tool for ecohydrological distribution modelling[J]. Ecological Modelling, 207(2-4): 304-318.
[27]  27 Rodriguez-Galiano V F, Ghimire B, Rogan J, et al.2012. An assessment of the effectiveness of a random forest classifier for land-cover classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 67: 93-104.
[28]  28 Wu F L. 2002. Calibration of stochastic cellular automata: the application to rural-urban land conversions[J]. International Journal of Geographical Information Science, 16(8): 795-818.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133