Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata     

Tingting Xu  Jay Gao  Giovanni Coco 《International journal of geographical information science》2019,33(10):1960-1983

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.  相似文献   

Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District   总被引：2，自引：0，他引：2  

Jun Yang  Andong Guo  Yonghua Li  Yuqing Zhang  Xueming Li 《地理信息系统科学与遥感》2019,56(3):388-405

In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation.  相似文献   

河南区域经济增长俱乐部趋同研究   总被引：13，自引：0，他引：13  

覃成林  唐永 《地理研究》2007,26(3):548-556

1990~2004年,河南区域经济增长形成了高收入、中高收入、中低收入和低收入 4个趋同俱乐部。高收入和低收入趋同俱乐部的稳定性最大且连片扩张,区域经济增长水平和空间结构的两极分化日趋增强。总体上,一个区域的"邻居区域环境"对其经济增长及其向何种趋同俱乐部转移有规律性的影响。低收入区域的经济增长及其向中低收入趋同俱乐部转移的概率与其邻居区域的经济增长水平正相关。中低收入区域更倾向于向低收入趋同俱乐部转移。对于中高收入区域,与经济增长水平越高的区域为邻,其向高收入趋同俱乐部转移的概率越大;反之,与经济增长水平越低的区域为邻,其向中低收入趋同俱乐部转移概率也越大。高收入区域的经济增长及稳定性受邻居区域的影响比较小。  相似文献   

基于灰色马尔科夫链预测系统的设计与实现   总被引：2，自引：0，他引：2  

杨灿  杨敏华  汪齐松 《测绘科学》2009,34(6)

通过对灰色GM[1,1]模型的算法改进,将改进后的灰色模型与马尔科夫链结合,既可以发挥灰色系统预测精确的特点,又可以利用马尔科夫链对准确预测波动性数据的优势。在灰色马尔科夫链模型的算法基础上,采用Visual Studio 2005开发环境,进行灰色马尔科夫链预测系统的设计。最后利用南方某地区十年来土地利用数据进行系统验证,结果表明,灰色马尔科夫链模型能很大地提高预测的精度和效果,符合实际要求。  相似文献   

Made in Japan: The Japanese market and herring roe production and management in Canada's southern Gulf of St. Lawrence     

Lenore Burke  John Phyne 《Marine Policy》2008

The herring roe fishery in Canada's southern Gulf of St. Lawrence is a productive fishery that lies outside the confines of privatized arrangements. Yet, this fishery is compromised by its dependence on the Japanese seafood market. Consideration of the efficiency of this fishery needs to account for the role assumed by this market in setting fishing practices. A more ecologically and economically efficient use of the herring resource requires greater attention to supply chain integration by local harvesters, processors and buyers prior to engaging with distant markets. This is necessary regardless of the property rights regime that is in place.  相似文献   

关于非齐次马氏链的强极限定理     

刘杰  纪灵军  梁佩佩 《成都信息工程学院学报》2009,24(1)

主要研究了非齐次马氏链的强极限定理.首先应用鞅差序列收敛定理给出了关于非齐次马氏链的任意k元函数一类平均值的极限定理.最后得到一系列相关状态序偶出现频率的推论.  相似文献   

几种非线性滤波算法的性能比较与分析          下载免费PDF全文

余春平  李广云  张冠宇 《海洋测绘》2008,28(6):43-45

在非线性状态估计中,传统的扩展卡尔曼滤波通过线性化来实现高斯近似,由于截断误差的存在很难保证估计精度;而基本粒子滤波容易出现粒子退化,导致滤波发散。针对粒子滤波的两个基本假设：蒙特卡罗假设和重要采样假设,采用蒙特卡罗随机链的方法来提高粒子的多样性,并利用无味卡尔曼滤波来产生更高精度的替代分布,发展了无味粒子滤波。通过仿真实验证明,相比较扩展卡尔曼滤波和基本粒子滤波,改进后的无味粒子滤波算法性能更优越,对含有非线性非高斯的状态估计问题有更好的滤波效果。  相似文献   

Simulations of realistic granular soils in oedometer tests using physics engine     

Hantao He  Junxing Zheng 《国际地质力学数值与分析法杂志》2020,44(7):983-1002

Discrete element method (DEM) has become a preeminent numerical tool for investigating the mechanical behavior of granular soils. However, traditional DEM uses sphere clusters to approximate realistic particles, which is computationally demanding when simulating many particles. This paper demonstrates the potential of using a physics engine technique to simulate realistic particles. The physics engines are originally developed for video games for simulating physical and mechanical processes that occur in the real world to produce realistic game experiences. The simulation accuracy and efficiency of physics engines have been significantly improved in the last two decades allowing them to be used as a scientific tool in many disciplines. This paper introduces modeling methodologies of physics engine including realistic particle representation and the contact model. Then, oedometer tests are simulated using realistic particles scanned by X-ray computed tomography (X-ray CT). The simulation results agree well with experimental results. This paper demonstrates that physics engines can output contact parameters for geotechnical analysis and force chains for visualization.  相似文献   

Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques   总被引：3，自引：0，他引：3  

Jialv He  Yao Yao  Ye Hong  Zhang Jinbao 《International journal of geographical information science》2018,32(10):2076-2097

Along with the gradually accelerated urbanization process, simulating and predicting the future pattern of the city is of great importance to the prediction and prevention of some environmental, economic and urban issues. Previous studies have generally integrated traditional machine learning with cellular automaton (CA) models to simulate urban development. Nevertheless, difficulties still exist in the process of obtaining more accurate results with CA models; such difficulties are mainly due to the insufficient consideration of neighborhood effects during urban transition rule mining. In this paper, we used an effective deep learning method, named convolution neural network for united mining (UMCNN), to solve the problem. UMCNN has substantial potential to get neighborhood information from its receptive field. Thus, a novel CA model coupled with UMCNN and Markov chain was designed to improve the performance of simulating urban expansion processes. Choosing the Pearl River Delta of China as the study area, we excavate the driving factors and the transformational relations revealed by the urban land-use patterns in 2000, 2005 and 2010 and further simulate the urban expansion status in 2020 and 2030. Additionally, three traditional machine-learning-based CA models (LR, ANN and RFA) are built to attest the practicality of the proposed model. In the comparison, the proposed method reaches the highest simulation accuracy and landscape index similarity. The predicted urban expansion results reveal that the economy will continue to be the primary factor in the study area from 2010 to 2030. The proposed model can serve as guidance in urban planning and government decision-making.  相似文献   

基于多目标遗传算法和FLUS模型的西北农牧交错带土地利用优化配置   总被引：1，自引：0，他引：1  

杨露  颉耀文  宗乐丽  邱天  焦继宗 《地球信息科学学报》2020,22(3):568-579

西北农牧交错带生态环境脆弱,区位特殊性和生态重要性使其在我国社会经济发展和生态环境保护方面具有重要战略意义。通过对该区域进行土地利用优化配置,使有限的土地资源支撑起生态环境保护和经济发展的重任是本文的出发点。多目标遗传算法和FLUS模型的应用可以从多方面（数量结构、空间布局、综合效益）完善土地利用优化配置,为土地利用优化配置提供更多的选择方案。本文选用多目标遗传算法和FLUS模型对该区域进行2025年的土地利用变化模拟,通过设置自然发展、生态保护优先、经济发展优先、生态-经济均衡4种情景,探讨了如何在兼顾生态环境保护与社会经济发展的情况下进行土地利用的优化配置。结果表明,基于生态-经济均衡情景下的优化方案,土地利用类型的数量结构和空间布局更为合理,其综合效益优于另外3种情景。该情景在合理限制经济发展速度的前提下,使生态建设获得稳定发展,其经济效益较生态保护优先情景下增长了8.96%,生态效益较经济发展优先情景下增长了0.77%,在生态保护与经济发展2种目标之间达到平衡,为西北农牧交错带的土地利用规划提供了决策辅助。  相似文献