Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.     

Late Quaternary chironomid community structure shaped by rate and magnitude of climate change

Much is known about how climate change impacts ecosystem richness and turnover, but we have less understanding of its influence on ecosystem structures. Here, we use ecological metrics (beta diversity, compositional disorder and network skewness) to quantify the community structural responses of temperature-sensitive chironomids (Diptera: Chironomidae) during the Late Glacial (14 700–11 700 cal a bp ) and Holocene (11 700 cal a bp to present). Analyses demonstrate high turnover (beta diversity) of chironomid composition across both epochs; however, structural metrics stayed relatively intact. Compositional disorder and skewness show greatest structural change in the Younger Dryas, following the rapid, high-magnitude climate change at the Bølling–Allerød to Younger Dryas transition. There were fewer climate-related structural changes across the early to mid–late Holocene, where climate change was more gradual and lower in magnitude. The reduced impact on structural metrics could be due to greater functional resilience provided by the wider chironomid community, or to the replacement of same functional-type taxa in the network structure. These results provide insight into how future rapid climate change may alter chironomid communities and could suggest that while turnover may remain high under a rapidly warming climate, community structural dynamics retain some resilience.     

典型断陷盆地流域生态系统服务价值时空差异研究

以典型断陷盆地——小江流域为研究对象,以1982年的TM影像、2003年的Landsat ETM+影像和2016年的Landsat OLI影像为数据源,结合云南省测绘局所提供的150 000的土地利用状况图,采用 Costanza 公式对生态系统服务价值进行评估,精细刻画出云南小江流域“盆—山”共存的地质分异结构下生态系统服务价值的时空变化特征。结果表明:（1）小江流域以农田生态系统为主,研究期内生态系统格局总体稳定,耕地、建设用地、林地和园地都有不同程度的增加,而未利用地面积减少,水域面积基本不变;（2）1982-2016年,研究区生态系统服务总价值呈上升趋势,1982、2003和2016年生态系统服务总价值分别为90.11×108元、105.65×108元和136.81×108元,耕地、林地、园地和水域的生态系统服务价值整体上升,而未利用地的生态系统服务价值减少;（3）整体上,小江流域南部和东部生态系统服务价值增加明显,而中部和西部地区生态系统服务价值呈阶段性变化或持续下降趋势,小江岩溶河谷区的生态系统服务价值最高,而沉积平坝区的生态系统服务价值密度最低,且为唯一一个衰退型生态系统服务功能区;（4）研究期内小江流域各项生态系统服务价值均呈上升趋势,调节服务居主导地位,且以气候调节服务价值最高。小江流域生态系统服务价值缺乏弹性,水域生态系统面积变化对研究区生态系统价值的变化具有放大作用。实施封山育林、退耕还林、小流域治理工程等石漠化综合治理工程可增加生态系统服务价值。      

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.     

汕头市PM2.5的气象要素影响分析及预报研究

利用2014—2017年汕头市PM_2.5的日浓度资料、以及汕头市国家基准气象观测站的同期地面气象资料,重点分析了汕头市PM_2.5浓度的变化特征以及风、混合层厚度、降水等气象条件对PM_2.5浓度的影响,同时探讨了污染物浓度变化的成因。在此基础上,根据汕头市的气候特点,采用BP （Back-Propagation）人工神经网络方法针对汛期和非汛期分别建立了PM_2.5质量浓度预报模型。结果表明：与多数内陆城市不同,汕头市PM_2.5浓度日变化为单峰型,这与汕头地处沿海受海陆风影响有关;PM_2.5浓度日峰值出现在08时左右,除早高峰污染物排放增加的因素外,与早晨时段的低风速环境有关;PM_2.5日均浓度随着风速的增大呈现减小趋势,PM_2.5日均浓度与08时混合层厚度显著相关（相关系数为-0.143）;汕头市非汛期PM_2.5浓度比汛期高,这与汕头市的亚热带季风气候特征有关,汛期各量级降水（暴雨以上除外）对PM_2.5的清除效果无明显差别,而非汛期降水对PM_2.5浓度有明显清除作用;BP人工神经网络模型的预报效果表明,汛期和非汛期的PM_2.5级别命中率TS分别为100%和90.3%,准确指数分别为87.7%和89.9%,总体预报效果良好。不同时期预报模型出现正误差的数量和程度均大于负误差,汛期预报模型在有强降水发生时误差较大,而非汛期预报模型在有冷空气入侵时误差较大。     

A Review of Atmospheric Electricity Research in China from 2011 to 2018

Atmospheric electricity research has been conducted actively in China, having profited from the development and application of high temporal and spatial resolution lightning detection and location technologies. This paper reviews the scientific advances made in the field of atmospheric electricity in China from 2011 to 2018, covering the following five aspects:(1)lightning detection and location techniques;(2) discharge processes and parameters associated with rocket-triggered lightning;(3) physical processes in natural lightning and attachment to the ground;(4) lightning activities and charge structure in different thunderstorms; and(5) effects of thunderstorms on the upper atmosphere. In addition, some outstanding questions for future research are outlined.     

Measuring,mapping and quantifying the effects of trust and informal communication on transboundary collaboration in the Great Lakes fisheries policy network

Ecosystem-based management of fisheries and other transboundary natural resources require a number of organizations across jurisdictions to exchange knowledge, coordinate policy goals and engage in collaborative activities. Trust, as part of social capital, is considered a key mechanism facilitating the coordination of such inter-organizational policy networks. However, our understanding of multi-dimensional trust as a theoretical construct and an operational variable in environmental and natural resource management has remained largely untested. This paper presents an empirical assessment of trust and communication measures applied to the North American Great Lakes fisheries policy network. Using a scale-based method developed for this purpose, we quantify the prevalence of different dimensions of trust and in/formal communication in the network and their differentiated impacts on decision-making and goal consensus. Our analysis reveals that calculation-based ‘rational trust’ is important for aligning mutual goals, but relationship-based ‘affinitive trust’ is most significant for influencing decision-making. Informal communication was also found to be a strong predictor of how effectively formal communication will influence decision-making, confirming the “priming” role of informal interactions in formal inter-agency dealings. The results also show the buffering and interactive functions of these components in strengthening institutional resilience, with procedural trust undergirding the system to compensate for a lack of well-developed relationships. Overall, this study provides evidence to suggest that informal communication and multi-dimensional trust constitute a crucial element for improving collaboration and reducing conflict in the networked governance of transboundary natural resource systems.     

基于小波长短期记忆网络的风电功率超短期概率预测

随着大规模的风电并网,风电所具有的间歇性与随机性对电力系统的稳定性产生了很大的影响,风电功率预测成为当前解决该问题重要的方式之一.本文利用长短期记忆（LSTM）网络良好的时序记忆特性,将小波分解技术与LSTM深度网络结合,提出基于小波长短期记忆网络的风电功率超短期概率预测模型.首先通过小波分解技术将原始时间序列进行平稳化处理,再建立各子序列样本的LSTM网络预测模型,借助最大似然估计法估计预测误差的高斯分布函数,最终实现对未来4 h时刻的风电功率概率区间预测.最后,采用中国东北某风电场数据对所提方法进行算例分析,结果表明,将小波分解与深度学习方法结合可以较好地提高预测的精度,提高概率预测的区间可靠性.     

东亚副热带西风急流的变化特征

利用1979-2013年NCEP/NCAR再分析资料研究了东亚副热带西风急流的变化特征。结果表明:东亚副热带西风急流中心位置、强度有明显的季节变化,冬季偏东偏南、强度最强,夏季偏北偏西、强度最弱。冬半年东亚副热带西风急流南界、北界年际变化的幅度大,夏半年幅度较小。冬季、春季东亚副热带西风急流范围较大,秋季、夏季小,一年内,偏大年或偏小年的出现不具有季节的连续性。