A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

Mobile Agent系统结构及其关键技术

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DISASTER ANALYSIS AND COUNTERMEASURES OF LAND SUBSIDENCE CAUSED BY COAL CUTTING IN CHINA

In recent years, many coal-producing countries have paid great attention to the land subsidence causedby coal cutting. In China, because of the dense population in coalfield areas, the land subsidence hazard is more seri-ous. After a brief analysis on the mechanism of land subsidence, this paper gives a comprehensive and systematical ac-count on all kinds of hazards caused by the land subsidence in China. The study shows that land subsidence has endan-gered land, buildings, traffic and communication lines, dykes and dams. It also causes damage to ecological and socialenvironment. In order to lessen the hazard of land subsidence, preventive measures should be taken to reduce the col-lapse amount, such as extraction with stowing, banded mining system, succession and coordination mining system, orhigh-pressure mudflow between rock strata. Measures of reinforcing or moving certain buildings should also be taken toreduce the destructive degree. In order to harness the subsidence land and bring them under control for fanning, mea-sures should be taken such as filling with spoil or fine breeze, excavating the deeper and covering the shallower land.     

风沙路基施工技术初探

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新疆塔中南坡奥陶系的地层缺失和沉积相变化

按照奥陶系内部6个组沉积的时间片段拟定塔中南坡不同区块存在不同程度的缺失。部分关键层段的牙形石和几丁虫组合特征证明一间房组和恰尔巴克组在塔中部分井区是存在的,但恰尔巴克组的分布范围最狭窄。总体上,塔中南坡隆起区地层缺失较多,古城墟隆起基本完整。据缺失状况和岩相展布,显示塔中南坡的沉积单元具有由东往西迁移的特征,且各时段迁移的距离与速度存在较大差异。     

大凌河流域河道生态需水量估算

本文从大凌河流域存在的环境问题出发,分别估算了各水资源分区的河道生态需水量（包括生态基流量、自净需水和输沙需水）及各区的河道生态需水贡献量。提出了使用偏枯年（75％保证率）水文数据估算河道生态需水量的方法．弥补了Tennant法、枯水季最小流量法等方法计算结果偏大或偏小的不足。结果表明,大凌河的4个河段（建凌站以上、建凌站至朝阳站、朝阳站至义县站、义县站至锦县段）的河道分区需水量分别为0．55×10^8m^3、5．21×10^8m^3、3．9×10^8m^3和1．34×10×10^8m^3。锦县水文站以上河道生态需水总量11×10^8m^3,占地表水总资源量的59．75％,因此．大凌河地表水资源取用率应在40％以下。     

相山铀矿田成矿物质来源探讨

铀源分析是铀矿床成因研究中的关键问题.在分析区域铀丰度特征的基础上,根据区域成矿物质在地质历史过程中的时空演化,结合相山矿田矿石铅同位素组成及岩、矿石微量元素地球化学对矿田铀源分析的指示,认为下寒武统地层为区域铀源层,相山火山盆地是区域成矿物质的"汇"区,岩浆及期后热液是铀的载体,即岩浆作用实现了铀从"源"到"汇"之间的物质转移.在岩浆演化过程中,铀向气液转移,为铀成矿提供了物质基础,岩浆期后流体-岩石相互作用,也促使了基底片岩、流纹英安岩中的部分铀进入成矿溶液.     

基于MapGIS的土地利用数据库建设实践——以赣州市为例

随着"数字国土工程"的启动,国土资源信息化管理得到了各级政府的高度重视,而土地利用数据库的建设是"数字国土工程"中基础数据库的重要组成部分.文章通过分析桌面地理信息系统MapGIS的功能,结合赣州市土地利用现状图的数字化实践,总结出用MapGIS进行土地利用数据库建库的流程,并对数据库建设中应特别注意的质量问题进行了探讨.     

中国东北依兰地区块状蓝片岩的地球化学特征

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黔西南水银洞金矿为产于上二叠统龙潭组中的超大型、高品位金矿床,矿床由多个金矿体组成.工业金矿体顺层产于龙潭组含煤地层所夹的玄武质、硅质、生物碎屑碳酸盐岩中.对水银洞金矿赋矿地层岩石以及矿石显微组构进行了研究,矿石中主要载金矿物为细粒黄铁矿,具再生边、粒内孔隙、铸模孔隙结构及镶边生物矿化构造、铸模式生物矿化构造,是一典型的具沉积一成岩组构.金矿化与硅化有密切的成因联系,金与SiO2来源于峨嵋山玄武岩的喷溢活动,主要金矿体更多显示与峨嵋山玄武岩准同生特征.矿床为沉积一成岩弱改造成囚金矿床.