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.     

Optical Flash of GRB 990123: Constraints on the Physical Parameters of the Reverse Shock

The optical flash accompanying GRB 990123 is believed to be powered by the reverse shock of a thin shell. With the best-fit physical parameters for GRB 990123 and the assumption that the parameters in the optical flash are the same as in the afterglow, we show that: 1) the shell is thick rather than thin, and we have provided the light curve for the thick shell case which coincides with the observation; 2) the theoretical peak flux of the optical flash accounts for only 3×10~-4 of the observed. In order to remove this discrepancy, the physical parameters, the electron energy and magnetic ratios, εe and εB, should be 0.61 and 0.39, which are very different from their values for the late afterglow.     

Silicon limitation on primary production and its destiny in Jiaozhou Bay, China——Ⅳ：Study on cross-bay transect from estuary to ocean

The authors analyzed the data collected in the Ecological Station Jiaozhou Bay from May 1991 to November 1994, including 12 seasonal investigations, to determine the characteristics, dynamic cycles and variation trends of the silicate in the bay. The results indicated that the rivers around Jiaozhou Bay provided abundant supply of silicate to the bay. The silicate concentration there depended on river flow variation. The horizontal variation of silicate concentration on the transect showed that the silicate concentration decreased with distance from shorelines. The vertical variation of it showed that silicate sank and deposited on the sea bottom by phytoplankton uptake and death, and zooplankton excretion. In this way, silicon would endlessly be transferred from terrestrial sources to the sea bottom. The silicon took up by phytoplankton and by other biogeochemical processes led to insufficient silicon supply for phytoplankton growth. In this paper, a 2D dynamic model of river flow versus silicate concentration was established by which silicate concentrations of 0.028–0.062 μmol/L in seawater was yielded by inputting certain seasonal unit river flows (m³/s), or in other words, the silicate supply rate; and when the unit river flow was set to zero, meaning no river input, the silicate concentrations were between 0.05–0.69 μmol/L in the bay. In terms of the silicate supply rate, Jiaozhou Bay was divided into three parts. The division shows a given river flow could generate several different silicon levels in corresponding regions, so as to the silicon-limitation levels to the phytoplankton in these regions. Another dynamic model of river flow versus primary production was set up by which the phytoplankton primary production of 5.21–15.55 (mgC/m²·d)/(m³/s) were obtained in our case at unit river flow values via silicate concentration or primary production conversion rate. Similarly, the values of primary production of 121.98–195.33 (mgC/m²·d) were achieved at zero unit river flow condition. A primary production conversion rate reflects the sensitivity to silicon depletion so as to different phytoplankton primary production and silicon requirements by different phytoplankton assemblages in different marine areas. In addition, the authors differentiated two equations (Eqs. 1 and 2) in the models to obtain the river flow variation that determines the silicate concentration variation, and in turn, the variation of primary production. These results proved further that nutrient silicon is a limiting factor for phytoplankton growth. This study was funded by NSFC (No. 40036010), and the Director's Fund of the Beihai Sea Monitoring Center, the State Oceanic Administration.     

Study on numerical simulation to water lowering in an open coal mine digging

In view of the situation of excavation of open coal mine for the underground water disaster,we should carry out simulation studies for the numerical value of the water lowering project and improve the accuracy and the level of the water lowering project.On the basis of the hydrological geological conditions of certain open mine digging,a more reasonable seepage numerical model was built according to MODFLOW.It was simulated in advance that the process of the confined water level descending with the time,and combining with the actual observations to test the correctness of the model.The calculation showed that the results coincided well with the results of actual measurement.Based on this,different water lowering numerical simulations were built for the open coal mine digging.It could be simulated and forecast that the changes of the groundwater level in drainage process within and outside the mine pit,and it was quantitatively assessed that the possible water lowering result of the opencast water drainage process,which provide an important basis for the actual water lowering project and the possible project disposal.     

中国-俄罗斯原油管道工程(漠河-大庆段)冻土工程地质考察与研究进展

在2005-2007年期间,先后3次对中国-俄罗斯原油管道漠河-大庆段沿线的冻土工程地质条件等进行科学考察,开展了冻土工程地质条件及其在气候变化和人类活动作用下的评价和预测研究.考察研究结果表明:管道沿线多年冻土在各类融区、季节冻土和水系等分隔作用下呈片状或岛状分布,沿线岛状、稀疏岛状及零星岛状占多年冻土区段的40%左右;管道沿线多年冻土随着气候的转暖和人类活动的影响不断退化.地形地貌单元、植被分布、地表水分条件的变化等局部因素对多年冻土的分布和地下冰的赋存产生重要的影响,管道沿线大约分布有50 km左右的沼泽湿地,其表层为腐殖质土及泥炭层,泥炭层下面分布着含土冰层或地下冰,是管道沿线最差的冻土工程地质地段;由于中俄原油管道沿线水系发育多,冻胀丘、冰椎和冰幔等不良冻土现象广泛分布.科学考察的成果为管道沿线冻土工程地质条件评价和预测、管道的稳定性影响分析以及后期的长期检测系统设置等研究奠定坚实的基础,进一步为即将开工的中俄原油管道漠河-大庆段工程的设计、施工提供科学依据.     

中国大别山东南缘首次发现大坝陨坑构造

简介首次在大别山东南缘安徽省境内发现的大坝陨坑构造。经初步评价,知陨坑呈椭圆形,长轴呈北北东向,长约19km,短轴长约12km,最大坑深约2km,是一个有中央隆起区的复杂型陨坑。在卫片上陨坑显示环形影像,地貌形态为一洼地。陨坑基座保存尚好,可对它直观和追索陨坑构造边界。形成于230Ma左右的各类撞击变质岩石系列齐全,其中含有柯石英等典型的撞击变质矿物及撞击碎理等超微构造,特别是在陨坑基座内壁普遍发现有鉴别陨坑构造最可靠的标志——干裂自角砾岩,都证明大坝环形影像是一个典型的陨星撞击坑构造。它的发现,具有很大的科学及经济意义,对今后褶皱山区寻找和研究陨坑构造具有示范和指导作用,同时大大丰富了建设大别山世界地质公园的地质依据。无庸置疑此发现将促进大别山旅游业的发展及陨星撞击科普知识的传播。     

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。     

南黄海灾害性地质研究

本文研完了南黄海10种灾害地质因素的性质、危害性及分布,对其进行了分类,在此基础上对本区之灾害地质进行了区划和评价。     

海藻糖类的气相色谱分析 Ⅱ.红藻及红藻多糖中D-半乳糖和3, 6-内醚半乳糖的测定

红藻中的糖类主要以多糖形式存在(琼胶、卡拉胶等)，其中主要的单糖组分是D-半乳糖和3, 6-内醚半乳糖，除此之外还含有少晕的木糖、葡萄糖及甘露糖等成分。由于各单糖的相互干扰，难以用比色等着通化学分析方法对每一种单糖准确地定性定量。作者曾研究过褐藻中单糖的气相色谱测定法, 但该法不能测定3, 6-内醚半乳糖，囚为这种内醚糖对酸水解不稳定。据Dutton和Nozawa等人报道, 将多糖经甲醇分解后进行气相色谱分析，结果令人满意。本文用琼胶(Difco bacto-agar)作代表，研究探讨了红藻胶甲醇-HCI分解的条件，确定了红藻胶中D-半乳糖和3, 6-内醚半乳糖的气相色谱分析方法，并将其应用于红藻中D-半乳糖和3, 6-内醚半乳糖的分析。     

黄河三角洲桩西101站潮沟地貌形态及其水动力学研究

在潮沟下游，横跨且生趣与潮沟的横断面设了５个观测站，进行了２４ｈ的水动力测量。结果表明，涨落潮流速流向不对称，在潮沟内，涨落潮流流速差别很大，涨潮流流速最大为９ｃｍ／２，而落潮流流速最大可达３８ｃｍ／ｓ。据推测，潮沟内之所以落潮流流速明显大于涨潮流流速，可能是由于潮沟与岸线斜交有关。