基于模糊神经网络的模糊空间对象生成方法研究

随着对GIS中的空间对象模型和自然地理特征表达的研究深入,模糊空间对象被提出。针对模糊空间对象表达的特点,提出了一种基于模糊神经网络的模糊空间对象生成方法。该方法将模糊技术与神经网络相结合,利用神经网络的学习能力调整模糊隶属函数和模糊规则,使系统具备自适应的特性。实验表明,这种基于模糊神经网络的生成模糊空间对象的方法比传统方法大大的提高了成果的精度。     

三维地震属性参数在煤层厚度预测中的应用

依托“西部煤炭资源高精度三维地震勘探技术”项目工程,对晋城某矿南翼大巷东南区5m×5m×1ms的三维地震数据体,采用三维地震属性参数预测煤层厚度及其变化规律：沿3煤层、15煤层10ms时窗提取地震属性42种,根据钻孔资料,计算出煤厚与地震属性相关系数;从中优选出相关系数大于0．35的地震属性,其中3煤层9个、15煤层10个;然后进行地震属性互相关分析,优选出与3煤、15煤层厚度相关系数较大的4种属性,建立预测煤厚的BP神经网络模型,分别选取3煤层12个、15煤层4个实测数据作为学习训练和测试样本,以钻孔地震属性作为学习样本,对网络进行训练,最终获得全区煤层厚度。经与预留钻孔成果资料对比,预测精度较高,结果可用。     

Automatic classification of volcanic earthquakes by using Multi-Layered neural networks

The application of neural networks as classifiers of seismic events is described with the aim of developing an automatic system for the classification of explosion quakes at the Stromboli volcano. The architecture of the network that we trained to identify four different classes of shocks was a Multi-Layer Perceptron, using the Back Error Propagation algorithm. Five different approaches for representing the information embedded in the seismograms, both in the time and in the frequency domain, were considered, and the results compared. The direct use of the time series of the shocks was not satisfactory. The auto-correlation function worked well, but in some cases it was misleading. A better performance was obtained with a frequency domain representation. Finally, the use of the envelope function did not work well. Combining parameters such as the auto-correlation and envelope functions can improve one source of error, but it may introduce new ones. The performance obtained highlights the importance of the data attributes used for the training of the network. Topologies with eight neurons in a single hidden layer gave, on average, the best results among the considered neural network structures. The overall results provide a large number of events (89% with the best performance) correctly classified, indicating that this automatic technique is reliable, and encouraging further applications in the field of volcanic seismology.     

人工神经网络在白云岩类型识别中的应用

利用地球化学数据,运用人工神经网络方法对美国密苏里州东南Ｂｏｎｎｅｔｅｒｒｅ组（寒武纪）滨海相的白云岩进行了分类、识别,判别率达１００％,结果表明,该方法性能良好,可望成为岩石分类、判别的有效手段。     

基于遥感影像大数据、卷积神经网络的福建省有效致灾雷电分布模型及应用

为了研究福建省有效致灾雷电的分布情况，基于福建省2004—2012年闪电定位数据及雷击人员伤亡数据、福建省L17级谷歌遥感影像瓦片，引入卷积神经网络模型对遥感影像所在区域是否属于人员活动的属性进行建模、训练和预测，得到福建省人员活动属性的格点产品，结合福建省历史雷电数据对有效致灾的雷电分布情况进行了分析，结果表明：①设计的遥感影像+CNN识别模型具有一定的可行性和准确率，通过显著性水平为0.01的假设检验；②福建省有63.55%的格点为无人员活动区域；③平均有45.36%的闪电落在无人员活动的区域，因地制宜地对其他致灾闪电进行预警是提高应急减灾服务效果的可行途径；④有效致灾雷电密度与历史雷击人员伤亡数据的相关性远大于常规雷电密度与历史雷击人员伤亡数据的相关性，有效致灾雷电分布在表征雷电灾害上比常规雷电分布更具有指示意义。     

Technologies of agency and performance: Tasmania Together and the constitution of harmonious island identity

Baldacchino [Baldacchino, G., 2002. Jurisdictional self-reliance for small island territories: considering the partition of Cyprus, The Round Table, 365, 349-360] has argued that the ‘troika’ of smallness, insularity and peripherality may incline island peoples (rather more than mainlanders?) to question the effects of economic globalization and be especially disposed to innovative approaches to development. He views jurisdictional capacity as integral to that task. Much of the literature on such issues relates to island nations, but this work focuses on Australia’s smallest and only island state of Tasmania, and thus on a sub-national jurisdiction. In what follows I explore the effects of an attempt to enrol Tasmanians in the creation and stabilization of a ‘2020 vision’ meant to be global in its reach, to focus on the particular strengths of the island state, and be innovative in advancing sustainable development. Known as Tasmania Together, the 20-year strategic vision outlines diverse economic, social and environmental goals assembled over two years via widespread consultations with the island’s communities of place and interest. For a time Tasmania Together generated significant debate about what it means to be an island people, and whether and to what extent Tasmanians’ future will be secured through economic globalization or localized endeavours premised on sustainability principles. Important to Tasmanians as well as to island studies, these rhetorics of social and spatial engagement also have salience beyond the borders of the island state, highlighting larger questions about the technologies of governmentality, agency and the performance of identity.     

Application of back-propagation networks in debris flow prediction

Debris flows have caused serious loss of human lives and a lot of damage to properties in Taiwan over the past decades. Moreover, debris flows have brought massive mud causing water pollution in reservoirs and resulted in water shortage for daily life locally and affected agricultural irrigation and industrial usages seriously. A number of methods for prediction of debris flows have been studied. However, the successful prediction ratio of debris flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable analytical model for occurrence predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective cumulative rainfall, and (7) effective rainfall intensity. A total of 178 potential cases of debris flows collected in eastern Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 93.82% demonstrates that the presented ANN model with seven significant factors can provide a stable and reliable result for the prediction of debris flows in hazard mitigation and guarding systems.     

Modeling the cyclic swelling pressure of mudrock using artificial neural networks

The stochastic nature of the cyclic swelling behavior of mudrock and its dependence on a large number of interdependent parameters was modeled using Time Delay Neural Networks (TDNNs). This method has facilitated predicting cyclic swelling pressure with an acceptable level of accuracy where developing a general mathematical model is almost impossible. A number of total pressure cells between shotcrete and concrete walls of the powerhouse cavern at Masjed–Soleiman Hydroelectric Powerhouse Project, South of Iran, where mudrock outcrops, confirmed a cyclic swelling pressure on the lining since 1999. In several locations, small cracks are generated which has raised doubts about long term stability of the powerhouse structure. This necessitated a study for predicting future swelling pressure. Considering the complexity of the interdependent parameters in this problem, TDNNs proved to be a powerful tool. The results of this modeling are presented in this paper.     

Managing sustainable farmed landscape through 'alternative' food networks: a case study from Italy

This paper focuses on a case study of an 'alternative' food network based in the Abruzzo National Park, Italy, to explore how ideas of sustainable farmland management can be expressed through broader understandings of developing networks of care concerned with local economies and societies, high-quality specialist food products, particular 'traditional' farming practices and livestock breeds, as well as the ecology of a farmed landscape. The scheme allows customers, internationally as well as in Italy, to 'adopt' a milking sheep on a large mountain farm. In return, adopters are sent food products from the farm. The adoption scheme is inter-twined with an agri-tourism project which provides accommodation, runs a restaurant and engages in educational activities. The scheme is the result of the individual initiative of its founder, and is associated with a strongly expressed ethical position concerning the value of sustaining valued local rural landscapes and lifestyles, and the importance of 'reconnecting' urban dwellers with rural areas, farming and 'quality' food production. Yet the localness of the scheme is sustained through wider national and international networks: volunteer and paid workers are drawn from several European countries, funding has been acquired from the EU LEADER programme, and internet and transport technologies are essential in connecting with and supplying an international customer base. The broader economy of care instanced in this case study draws attention to a need to develop strategies for sustainable farmland management constructed around wider programmes of social, economic and cultural, as well as environmental, concern.     

Can we sustain sustainable agriculture? Learning from small-scale producer-suppliers in Canada and the UK

There has been particular interest in 'alternative' food over the last 10 years, with many policymakers and researchers throughout the Minority World following a growing number of consumers and producers in supporting organic farming and a host of 'alternative' food networks. To date, there has been a tendency for theory and policy to emerge somewhat divorced from the grounded practices and experiences of producer-suppliers themselves within these networks. Urging a shift from 'alternativity' to 'sustainability' as a more critical and valuable tool to analyse food networks, this paper draws upon in-depth ethnographic research with small-scale producer-supplier case studies in south Wales and southern Ontario. In so doing it explores often overlooked voices and stories within sustainable food discourses. Focusing on the value of farmer-led understandings and responses, the paper highlights important implications for policymakers and consumers and outlines future research on sustainable food networks.