基于主成分分析的济宁市环境承载力研究

该文以济宁市环境承载力为研究对象,运用主成分分析法,从影响环境污染状况、环境治理状况、人口状况、经济发展状况、自然资源状况、物耗能耗状况等19个指标中,提取出影响环境承载力动态变化的3个主成分,对济宁市环境承载力年际变化进行了综合评价。结果表明:2006—2010年,济宁市环境承载力呈现逐年上升的趋势。该研究评价结果较好,可以为该地区的可持续开发规划提供理论参考。     

山东省蓬莱市西南王金矿床地质特征与资源前景

西南王金矿床位于栖霞-蓬莱成矿带内,是蓬莱地区西部比较典型的缓倾斜薄层石英脉型金矿床。矿体受NNE—NE向断裂控制,倾向SE,倾角15°~25°,呈薄层脉状,分布于郭家岭序列罗家单元的斑状中粒含黑云二长花岗岩和谭格庄序列牟家单元的片麻状细粒奥长花岗岩中。该文通过对矿床地质特征与矿体空间赋存规律的研究,认为矿床深部及周边区域仍有较好的资源前景。     

肥城市土地生态系统安全评价研究

在分析土地生态系统安全内涵的基础上,以肥城市为研究区域,选取24个指标构建土地生态系统安全评价指标体系,采用熵值法、综合指数法和GIS相结合的方法,对肥城市2000—2013年的土地生态系统安全状况进行分析。研究结果表明:14年来,肥城市土地生态系统安全状况处于波动状态,土地生态系统安全面临着诸多压力。最后提出了提高土地生态系统安全状况的对策建议。     

北方集中供热系统气象风险评估初探

供热管网爆裂、跑冒滴漏是北方集中供热城市面临的常见问题,供热管网出现大型故障往往是在室外寒冷的隆冬时节,如果间断或限额供热时间过长,将会造成严重的社会和经济影响。同时在极端低温情况下,可能会造成热源供应不足,出现限额供热现象,不能保证室内舒适度,影响人们的正常生活。本文提出了能源供应气象风险评估和热网维修寒冷风险评估方法,并进行了案例评估,以期为未来进行相关风险评估提供参考。     

基于神经网络的页岩微纳米孔隙微结构分析的正则化和最优化方法

页岩气成藏机理与页岩内部孔隙结构紧密相关,对页岩孔隙结构的研究成为页岩气勘探开发技术中至关重要的一环。页岩内部不同结构体组分对X-射线的吸收能谱不一样,这样就导致观测数据是由不同页岩组分衰减不同波段的X-射线构成的。经过对CT图像分割,能够获得页岩微孔结构的图像,尤其是获得有机质中孔隙类别、形状、尺寸、空间分布、连通特性。本文利用同步辐射X射线扫描重构的页岩CT数据,研究并设计基于多能CT图像的神经网络图像分割技术和算法,以期得到页岩体三维结构特征及空间分布,可以为建立有机质种类和无机矿物组成与微纳孔隙特征的联系以及最终实现页岩气的资源储量评估和勘探开发提供技术支持。     

音频大地电磁测深在齐大山铁矿王家堡子采区深部找矿中的应用

鞍山齐大山铁矿王家堡子采区产出鞍山式沉积变质型铁矿,铁矿石多以磁铁贫矿为主,局部产出富铁矿,目前钻探工程控制标高为-600 m,采区深部的资源潜力是急待解决的问题,为此对采区进行音频大地电磁测量和研究工作。齐大山铁矿的电性变化复杂,通过先期的实验剖面确定了矿区铁矿体的3种不同电阻率特征,然后对王家堡子采区的3760线、4050线、4500线进行音频大地电磁测量、数据处理和断面反演分析。推测3760线的低阻由磁铁贫矿引起,局部高阻为假象赤铁贫矿引起;推测4050线和4500线浅部的低阻由磁铁贫矿引起,深部的高阻为假象赤铁贫矿及磁铁贫矿引起。同时预测3760线、4050线和4500线西侧深部均有低电阻率显示,反映出隐伏铁矿床的存在特征,推测在-1 000 m标高以下仍存在有隐伏的富矿体或板状磁铁贫矿。经过钻探验证,获得了预期的找矿效果。     

A virtual globe-based time-critical adaptive visualization method for 3D city models

3D city models, which are important items of content on the virtual globe, are characterized by complicated structures and large amounts of data. These factors make the visualization of 3D city models highly dependent upon the performance of computer hardware. Thus, achieving the efficient rendering of 3D city models using different hardware performance levels represents one of the key problems currently facing researchers. This paper proposes a time-critical adaptive visualization method that first estimates the possible rendering time for each model according to the data structure of the model in addition to the CPU/GPU performance of the computer. It then dynamically adjusts the rendering level for each model based on the results of an estimation of the rendering time to ensure that the final scene can be completed within a given time. To verify the effectiveness and flexibility of this method, it is applied using different computers. The results show that the adaptive visualization method presented in this paper not only can adapt to computers with different levels of performances but also demonstrates an obvious improvement in the time estimation precision, visual effects, and optimization speed relative to existing adaptive visualization methods.     

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.     

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.