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   

基于机器学习的海洋环境预报订正方法研究     

许立兵  王安喜  汪纯阳  陈悦  陈昱文  周峥  陈幸荣  邢建勇  刘克威  黄小猛 《海洋通报》2020,39(6):695-704

结合中尺度数值模式 WRF 预报数据和 ERA5 再分析资料，利用机器学习方法对 WRF 预报场的风场、温度、气压进行预报订正。采用 ERA5 作为真值，与原始 WRF 预报相比，利用随机森林模型可以将预报结果整体均方根误差降低 44%以 上，利用深度神经网络模型可以将预报结果整体均方根误差降低 34%以上。通过随机森林模型实验得到不同输入特征对预报要素的影响程度，分析了关键的预报订正因子。  相似文献   

A surface network based method for studying urban hierarchies by night time light remote sensing data   总被引：1，自引：0，他引：1  

Bin Wu  Qiusheng Wu  Zuoqi Chen  Jianping Wu 《International journal of geographical information science》2019,33(7):1377-1398

Urban hierarchies are closely related to economic growth, urban planning and sustainable urban development. Due to the limited availability of reliable statistical data at fine scales, most existing studies on urban hierarchy characterization failed to capture the detailed urban spatial structure information. Previous studies have demonstrated that night time light data are correlated with many urban socio-economic indicators and hence can be used to characterize urban hierarchies. This paper presents a novel method for studying urban hierarchies from night time light data. Night time light data were first conceptualized as continuous mathematical surfaces, termed night time light surfaces. From the morphology of these surfaces the corresponding surface networks were derived. Hereafter, a night time light intensity (NTLI) graph was defined to describe the morphology of the surface network. Then, structural similarity between the night time light surfaces of any two different cities was calculated via a threshold-based maximum common induced graph searching algorithm. Finally, urban hierarchies were defined on the basis of the structural similarities between different cities. Using the 2015 annual NPP-VIIRS night time light data, the urban hierarchies of 32 major cities in China were successfully examined. The results are highly consistent with the reference urban hierarchies.  相似文献   

A network theory approach for a better understanding of overland flow connectivity          下载免费PDF全文

Rens J. H. Masselink  Tobias Heckmann  Arnaud J. A. M. Temme  Niels S. Anders  Harm P. A. Gooren  Saskia D. Keesstra 《水文研究》2017,31(1):207-220

Hydrological connectivity describes the physical coupling (linkages) of different elements within a landscape regarding (sub‐) surface flows. A firm understanding of hydrological connectivity is important for catchment management applications, for example, habitat and species protection, and for flood resistance and resilience improvement. Thinking about (geomorphological) systems as networks can lead to new insights, which has also been recognized within the scientific community, seeing the recent increase in the use of network (graph) theory within the geosciences. Network theory supports the analysis and understanding of complex systems by providing data structures for modelling objects and their linkages, and a versatile toolbox to quantitatively appraise network structure and properties. The objective of this study was to characterize and quantify overland flow connectivity dynamics on hillslopes in a humid sub‐Mediterranean environment by using a combination of high‐resolution digital‐terrain models, overland flow sensors and a network approach. Results showed that there are significant differences between overland flow connectivity on agricultural areas and semi‐natural shrubs areas. Significant positive correlations between connectivity and precipitation characteristics were found. Significant negative correlations between connectivity and soil moisture were found, most likely because of soil water repellency and/or soil surface crusting. The combination of structural networks and dynamic networks for determining potential connectivity and actual connectivity proved a powerful tool for analysing overland flow connectivity. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

Knowledge integration in sustainability governance through science-based actor networks     

《Global Environmental Change》2021

Understanding how science, technology and innovation can best help to accelerate progress in achieving sustainable development remains a grand challenge for researchers and practitioners. In the context of the global consultation process for preparing a post-2015 Sustainable Development Agenda, various science-based actor networks have emerged, aiming to translate research into political decision-making and to inform transformations towards sustainability. Over the last years, these networks seem to have taken an ever-growing role in structuring the science-policy interface in global sustainability governance. The question arises, however, how they understand and organize ‘scientific knowledge integration’ in sustainability politics.This study offers a structured comparison of twelve global science-based actor networks engaged in the implementation of the Sustainable Development Goals. It shows that these networks use two types of strategies to foster scientific knowledge integration in sustainability governance. A new framework emerges, in which each strategy corresponds to two main approaches of scientific knowledge integration: The entrepreneurial strategy generally seeks to advance advice-oriented and solution-oriented knowledge processes, while assessment-oriented and learning-oriented processes in scientific knowledge integration are mainly promoted through a mediating strategy.  相似文献   

Remote sensing of water depths in shallow waters via artificial neural networks     

Özçelik Ceyhun  Arısoy Yalçın 《Estuarine, Coastal and Shelf Science》2010

Determination of the water depths in coastal zones is a common requirement for the majority of coastal engineering and coastal science applications. However, production of high quality bathymetric maps requires expensive field survey, high technology equipment and expert personnel. Remotely sensed images can be conveniently used to reduce the cost and labor needed for bathymetric measurements and to overcome the difficulties in spatial and temporal depth provision. An Artificial Neural Network (ANN) methodology is introduced in this study to derive bathymetric maps in shallow waters via remote sensing images and sample depth measurements. This methodology provides fast and practical solution for depth estimation in shallow waters, coupling temporal and spatial capabilities of remote sensing imagery with modeling flexibility of ANN. Its main advantage in practice is that it enables to directly use image reflectance values in depth estimations, without refining depth-caused scatterings from other environmental factors (e.g. bottom material and vegetation). Its function-free structure allows evaluating nonlinear relationships between multi-band images and in-situ depth measurements, therefore leads more reliable depth estimations than classical regressive approaches. The west coast of the Foca, Izmir/Turkey was used as a test bed. Aster first three band images and Quickbird pan-sharpened images were used to derive ANN based bathymetric maps of this study area. In-situ depth measurements were supplied from the General Command of Mapping, Turkey (HGK). Two models were set, one for Aster and one for Quickbird image inputs. Bathymetric maps relying solely on in-situ depth measurements were used to evaluate resultant derived bathymetric maps. The efficiency of the methodology was discussed at the end of the paper. It is concluded that the proposed methodology could decrease spatial and repetitive depth measurement requirements in bathymetric mapping especially for preliminary engineering application.  相似文献   

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model   总被引：10，自引：0，他引：10  

Biswajeet Pradhan  Ahmed M. Youssef  Renuganth Varathrajoo 《地球空间信息科学学报》2010,13(2):93-102

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.  相似文献   

异构传感器网络基于身份的认证及密钥协商方案     

付小晶  张国印  马春光 《武汉大学学报(信息科学版)》2010,(5)

结合身份密码体制,提出了一个基于身份和地理位置信息的异构传感器网络(HSN)节点间的双向认证及密钥协商方案。不同性能节点之间采用不同的协议完成身份认证和密钥建立,充分发挥了高性能节点的能力,降低了低性能节点的能耗。该方案具有完美前向保密性和主密钥前向保密性,具有较好的抗节点伪造、节点复制和女巫攻击能力。分析与仿真表明,该方案具有较好的安全性。  相似文献   

径向基神经网络GPS高程转换方法     

杨金玲  曹先革  曹先密 《地理空间信息》2010,8(6):65-66,70

针对GPS高程转换问题,给出了基于径向基神经网络转换GPS高程的模型。用实际观测数据对该模型进行了试验,结果表明,用径向基神经网络转换GPS高程精度高于二次拟合法和BP神经网络法。径向基神经网络能够有效克服BP神经网络局部极小值的缺点,并且具有较高的收敛速度,在GPS高程转换方面具有广阔应用前景。  相似文献   

基于A- CGAN的深反射地震数据随机噪声压制方法研究          下载免费PDF全文

韩建光  王卿  许媛  刘志伟 《地质论评》2024,70(1):228-238

基于深度学习的地震数据噪声压制方法是当前地震数据去噪处理的重要方向。深度学习方法突破了传统滤波处理的局限,在对常规地震数据的噪声压制中表现出效率高、信噪分离效果好的特点。但针对深部弱有效反射数据,当前的深度学习方法特征提取能力有限,难以取得较好的去噪效果。笔者等结合深反射地震数据特点,针对当前深度学习噪声压制方法在特征提取及对数据集依赖上的局限,提出了基于注意力循环生成对抗网络(Attention Cycle- Consistent Generative Adversarial Networks,A- CGAN)的深反射地震数据随机噪声压制方法。借助循环一致生成对抗网络(Cycle- Consistent Generative Adversarial Networks,Cycle- GAN)的域映射思想,降低对数据集的要求。为了构建适用于深反射地震数据的去噪网络,从3个方面对Cycle- GAN进行改进：在Cycle- GAN的生成器（去噪器）中加入残差结构和注意力机制,用于加深网络深度和提高其特征提取能力;在Cycle- GAN的鉴别器中使用块判决,提高鉴别精度和准确度;在损失函数部分加入感知一致性损失函数,提升网络模型恢复纹理细节信息的能力。通过合成地震数据和实际深反射地震数据测试,验证了优化算法的有效性,体现了良好的应用价值。  相似文献