Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.     

Fuzzy set approaches to classification of rock masses

Rock mass classification is analogous to multi-feature pattern recognition problem. The objective is to assign a rock mass to one of the pre-defined classes using a given set of criteria. This process involves a number of subjective uncertainties stemming from: (a) qualitative (linguistic) criteria; (b) sharp class boundaries; (c) fixed rating (or weight) scales; and (d) variable input reliability. Fuzzy set theory enables a soft approach to account for these uncertainties by allowing the expert to participate in this process in several ways. Hence, this study was designed to investigate the earlier fuzzy rock mass classification attempts and to devise improved methodologies to utilize the theory more accurately and efficiently. As in the earlier studies, the Rock Mass Rating (RMR) system was adopted as a reference conventional classification system because of its simple linear aggregation.

The proposed classification approach is based on the concept of partial fuzzy sets representing the variable importance or recognition power of each criterion in the universal domain of rock mass quality. The method enables one to evaluate rock mass quality using any set of criteria, and it is easy to implement. To reduce uncertainties due to project- and lithology-dependent variations, partial membership functions were formulated considering shallow (<200 m) tunneling in granitic rock masses. This facilitated a detailed expression of the variations in the classification power of each criterion along the corresponding universal domains. The binary relationship tables generated using these functions were processed not to derive a single class but rather to plot criterion contribution trends (stacked area graphs) and belief surface contours, which proved to be very satisfactory in difficult decision situations. Four input scenarios were selected to demonstrate the efficiency of the proposed approach in different situations and with reference to the earlier approaches.     

London and Frankfurt in Europe's evolving financial centre network

The launch of the Euro and the location of the European Central Bank in Frankfurt initially was seen as a threat to London's pre-eminent position in European financial geographies. This paper explains why in fact this was not the case. To do this, the paper is divided in two. Firstly, it reviews the literatures that help to explain financial geographies. It is argued that we need to move away from investigating attribute properties such as financial turnover and instead examine the role of networks and interdependencies in producing financial geographies. Secondly, it identifies London's dominance and Frankfurt's growth as a complementary centre through quantitative analysis and then explains how European networks and interdependencies produce this, based on insights from interviews with investment bankers and insurance institution workers in the two cities.     

概率神经网络与BP网络模型在遥感图像分类中的对比研究

通过分析概率神经网络(以下称PNN)的基本结构及其训练算法,建立了卫星图像分类的概率神经网络模型,并通过实例对比分析了概率神经网络与BP网络分类模型的分类效果。实验表明,PNN图像分类方法在分类精度上优于误差反向传播神经网络模型,且分类时间相当,是一种有效的图像分类方法。     

我国国家大地网的地壳形变改正探讨

以2000网为例，根据1991～2001年累积的GPS重复观测资料，利用GIPSY软件建立具有1115个测点的地壳运动模型。在此基础上，采用双三次样条函数数值拟合法，模拟出2000网网点相对西安基准站的地壳形变改正值，并给出了效果图。     

基于三角网渐进式简化的等高线多尺度综合

提出了一种基于三角网渐进式简化的等高线多尺度综合方法,实验结果表明,其不仅可以较好地保留地形特征,而且可以完全避免等高线相交现象的产生.     

GPS高程转换的神经元网络方法分析

GPS高程转换是GPS高程应用的关键问题之一，试图应用神经元网络方法来进行GPS高程的转换，并在网络的拓扑结构与网络结构的改进等方面作了分析，通过GPS高程转换的二次曲面法和神经元网络法进行了比较试验，结果表明了神经元网络转换GPS高程的可行性和可靠性，对于应用神经网络转换GPS高程具有实际的指导意义。     

Black Holes and Loose Connections in a Global Urban Network

This paper introduces the concept of black holes and loose connections in a global urban hierarchy. Black holes are defined as large cities, with a population of over 3 million, that are not classified as world cities. The paper draws upon a classification that uses advanced producer services as an indicator of world city status. Large, nonworld cities are identified, and provisional ideas about explaining their position are outlined. Connectivity and population data are used in a simple regression analysis to identify loose connections: cities whose connectivity is less than that predicted by their population.     

陕西省信用联社市县级计算机网络防雷工程设计分析

“山西省卫星农网”依托其省、市、县三级专门服务于农业的信息交流平台,能够及时地让农户了解国家和地方的农业政策及办事程序等,成为农户和政府间的信息桥梁,为广大农村提供准确的各类气象信息,有效地指导了农业生产,大大地减小了天气灾害损失。山西卫星农网作为山西省规模较大的农业专业网站,其影响和作用正逐步地发挥出来。     

Computation of response spectra from mining tremors using neural networks

Underground coal and copper ore exploitation in two Polish mining regions causes mining tremors and a series of other negative phenomena in the environment. Although these tremors are strictly connected with human activity, they differ considerably from other paraseismic vibrations. The moment of their occurrence is not to be foreseen likewise for earthquakes. The main problem discussed in the paper was formulated as the neural network evaluation of a relation between mining tremor energies, epicentral distances and acceleration response spectra. Back-propagation neural networks with Resilient back-propagation learning method were used. Each input vector included information about the mining tremor energy and the epicentral distance. Values of acceleration response spectrum were expected as the outputs of neural networks. Neurally evaluated spectra were compared with spectra computed on the basis of experimental data. After the network is trained and tested, it can be used for mapping of new data of mining tremor energies and epicentral distances into the spectra. Then, what is the substantial advantage of neural approach, the prediction of acceleration response spectra can be performed without recording of surface vibrations. In the light of the results, it is visible that the presented way of computation of acceleration response spectra can be peculiarly applied to prognosis of mining tremors influences on structures.