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.     

ECOLOGICAL SERIES OF SOIL ANIMALS IN DARLIDAI MOUNTAIN

The ecological series of soil animals under the broad-leaved and pine mixed forest in Darlidai Mountainwas studied. Seven sample plots were selected according to different altitude gradients, which belong to different vegeta-tion types. By investigating and analyzing soil animals in every sample plot it is found that there are 45 groups and 1956individuals, which axe involved in 3 phylums, 7 classes, 16 orders, respectively. The altitude is a key factor which af-fects ecological series of soil animals. Both the groups and individuals of soil animals increase with altitude increasingunder certain conditions, which contrastes with ordinary cases, resulting from special micro-climate in studied area. Thegroups and individuls of soil animals are the most under the broad-leaved and pine forest on the top of the mountain, andthe least under Picea-Abies forest in the foot of the mountain.     

邻菲啰啉在测定亚铁中的保护效果

样品在分解过程中 ,亚铁易被空气部分氧化 ,致使测试结果偏低 ,采用在HF H2 SO4溶矿过程中加入邻菲卟罗啉 ,与亚铁形成络合物 ,对保护亚铁具有较好的效果。拟定了在塑料坩埚中加邻菲卟罗啉、H2 SO4及HF ,中高温电热板溶矿 ,容量法测定样品中的亚铁 ,方法精密度为 0 .2 3% ,样品加标准回收率 98.8%～ 10 1.9%。此方法具有较好的应用效果     

AR（P）参数估计协方差方法与其改进型方法的对比研究

定性分析协方差方法及其改进型在ＡＲ（Ｐ）参数估计中的应用，提供了一种估计参数的计算机上机流程图，比较了协方差方法及其改进型谱估计结果。     

富氏谱分析法模拟地壳垂直运动的地学依据及其策应力模型

应用地壳波浪与镶嵌构造学说对富氏谱分析法提取地壳垂直形变信息的科学性做了地质学意义上的阐释 ,并提出了根据多期形变资料提取特定波段上构造策应力的数学模型     

量子化学方法研究氯代苯酚类化合物对水生物毒性的构效关系

对20个氯代苯酚类化合物进行了CNDO/2量子化学计算,讨论了化合物电子结构与其对水生物翻车鱼(Bluegill)、花鳉鱼(Guppy)、和虾(Shrimp)半致死量之间的关系,分别获得了表示其构效关系的三个线性方程,它们的显著性均远高于α=0.01水平。结果表明:氯代苯酚化合物苯环上碳原子的兀电荷之和(∑Qπ_R)越大苯环上碳原子的净电荷之和(∑Q_R)越大,或LUMO轨道能(E_(LUMO))越低,均使化合物对Bluegill和Guppy水生物的毒性增大;而化合物苯环上羟基邻位的碳原子的兀电荷布居(P_3)越大,同时HOMO轨道能E_(HOMO)越高,则化合物对Shrimp毒性也越大。据此,可预测氯代苯酚类化合物对上述三种水生物的毒性。     

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.     

复杂模型重力位场快速正反演方法及应用

提要 本文详细讨论了一种三维重力位场快速正反演方法。作者在前人工作的基础上,对算法作了行之有效的改进,通过对反演中的不稳定因素进行各种理论模型试算,得出保证迭代反演稳定收敛的准则,编制出可在微型机IBM—PC上运行的人机对话式自动正反演程序。本文还对各种不均质模型进行了模似计算并将该方法应用于某含油气沉积盆地的双层界面构造研究,揭示出了储油有利地段。     

基于专家系统决策的多模式控制及其应用

将专家系统技术与常规控制、模糊控制、超前控制、仿人智能操作控制以及计算机控制技术相结合,设计了一种新的基于专家系统决策的多模式控制(MCDBES)。经试验和实际运行结果表明,MCDBES系统对未知数学模型的被控对象,具有良好的控制性能。本文提出的MCDBES已成功地应用于燃油陶瓷窑炉计算机控制系统。