Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks

Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy.     

Lesbian and Queer Sustainable Farmer Networks in the Midwest

Heteropatriarchy underpins contemporary U.S. agriculture, even within the alternative sector. This paper builds on the legacies of women farmers and farmers of color creating peer networks to circumvent heteropatriarchal hurdles by investigating how lesbian, bisexual, trans, and queer (LBTQ) sustainable farmers access human resources. If and how did the farmers encounter or resist heteropatriarchy in this process? Drawing on four years of ethnographic research with 40 LBTQ Midwest sustainable farmers, I argue that resources through government agencies, neighborhood farmers, and like-minded practitioners did not necessarily align with LBTQ farmers’ sustainable practices or queer identities. LBTQ farmers convened with others at the intersections of their queerness and sustainable practices formally, informally, and through the labor market to access human resources removed from heteropatriarchal domination. I conclude that LBTQ farmer networks bolster human resources in sustainable agriculture and conservation practices.     

长白山景区旅游安全风险动态评价研究

以长白山景区旅游安全为研究对象,以鱼骨图、动态贝叶斯、GIS技术等为基本研究方法,从研究区自然环境、社会环境及责任人为3个方面出发,筛选景区致险因子,构建景区旅游安全风险危险性评价指标体系,利用动态贝叶斯方法综合构建景区旅游安全风险动态评价模型;并以实测数据及景区统计数据为依据,划分景区旅游安全风险评价的4个动态时段,综合实现景区旅游安全风险动态风险评价。研究结果表明：① 中等以上风险区域呈条带状分布;② 高风险区域与主要景点重合;③ 长白山景区安全风险发生高概率的时段发生在第三个时段（12:00~14:00）;较高概率发生分别在第二个时段（10:00~12:00）与第四个时段（14:00~16:00）;中等概率发生较高的时段在第四个时段（14:00~16:00）;较低概率发生在第一个时段（8:00~10:00）。     

A surface network based method for studying urban hierarchies by night time light remote sensing data

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.     

结合深度学习和图割法的遥感影像建筑物检测

提出一种基于卷积神经网络和图割法的自动提取高分影像建筑物的方法。首先,通过卷积神经网络定位与检测建筑物的位置,逐一提取单个建筑物轮廓,利用检测结果分别建立建筑物和非建筑物的高斯混合模型（GMM）,然后结合最大流最小割的图像分割方式实现全局优化,完成建筑物初步提取,最后用形态学进行优化。通过试验证明了该方法的可行性。     

Geospatial data mining for digital raster mapping

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.     

大型GNSS观测网的并行计算

CGCS2000精化及其全球拓展需要采用最新的模型,处理长期积累的大型GNSS观测网数据,大型GNSS网联合、快速和协同解算是空间基准精化、维持与服务的重要技术方向.在大数据技术背景下,以并行计算、云计算为代表的高性能计算技术逐渐成为大规模数据处理的首选方法.针对海量、多源、异构GNSS数据在解算处理与平差分析等方面面...     

Environmental change and the informal plastic recycling networks of Kolkata

This article introduces plastic recycling networks in Kolkata, India, as a case to illustrate the contradictory entanglement of economic and environmental change in urban informal contexts of the Global South. In light of the prevailing environmental critique of informal plastic recycling in India, this article discusses plastic recyclers’ environmental impact and contribution as well as the potential to enhance the environmental and economic performance of their business. Network and chain approaches in the emerging field of Environmental Economic Geography are combined with the notion of social metabolism to conceptualize the entanglement of environmental and economic processes as well as socio-environmental inequalities entailed in recycling networks. The analysis reveals the impact that the heterogeneity and fluctuation of plastic waste supply has on the economic organization of recycling networks, giving rise to distinct forms of governance in its intermediate and down-stream segments. Such an integrated perspective also serves to explain why environmental upgrading is hard to achieve under prevailing circumstances by plastic recyclers in Kolkata. Based on an assessment of the social and political conditions of informality under which plastic recyclers operate, the adoption of common assumptions about ecological modernization imbuing parts of Environmental Economic Geography is called into question.     

哈大城市带网络结构韧性演化研究

网络结构韧性是城市空间组织未来发展的重要指向,是社会经济稳定性与恢复力的结构保障。论文借鉴复杂网络指标,构建城市网络结构韧性演化评价模型,以哈大城市带为例,对比分析多重城市网络结构韧性的演化特征。结果表明：① 多重城市网络层级性与匹配性存在分异,在层级性上,交通和金融网络趋于扁平,信息和创新网络趋于立体;在匹配性上,交通和创新网络分别表现为同配性与异配性且特征强化,金融和信息网络为异配性但特征削弱。② 网络结构韧性类型相对稳定,交通运输网络为“同配—核心边缘网络”,金融、信息与创新网络为“韧性网络”;韧性演化综合水平表现为创新合作网络>信息关联网络>0>金融联系网络>交通运输网络。③ 可以从整体结构、片区差异和要素流动三方面优化网络结构韧性演化水平。