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.     

Simulating methane emission from a Chinese rice field as influenced by fertilizer and water level

Natural and agricultural wetlands are considered to be the major sources of global atmospheric methane (CH₄). A one‐dimensional model was developed to simulate methane emission and used to examine the influence of various physical processes on the rate of methane emission. Three processes involved in the methane emission are implemented in the model: production, reoxidation and transport. Three transport pathways were considered: diffusion across water–air or soil–air interfaces, ebullition and diffusion through plants. These pathways are influenced by soil properties, plant growth, water‐table conditions, temperature and external inputs (e.g. fertilizer). The model was used to examine the seasonal variation of the methane emission at a rice field in Hunan, China, which was observed during a field experiment for consecutive (early and late) rice seasons in 1992. The observed seasonal variations of methane emission, and role of plants in transporting methane to the atmosphere, are captured by the model simulation. Further model applications were conducted to simulate effects of fertilizer and water‐level condition on the methane emission. The results indicate that unfermented organic fertilizer produces a higher methane emission rate than mineral fertilizer. The simulations with treatments of a deep‐water covering and constant moisture reduced the methane emission. The rice field study provides a framework for further development of the model towards simulations based on spatially distributed variables (e.g. water table, soil temperature and vegetation) at a regional scale. Copyright © 2003 John Wiley & Sons, Ltd.     

Glacier change and glacier runoff variation in the Tuotuo River basin,the source region of Yangtze River in western China

Glaciers in the Tuotuo River basin, western China, have been monitored in recent decades by applying topographical maps and high-resolution satellite images. Results indicate that most of glaciers in the Tuotuo River basin have retreated in the period from 1968/1971 to 2001/2002, and their shrinkage area is 3.2% of the total area in the late 1960s. To assess the influence of glacier runoff on river runoff, a modified degree–day model including potential clear-sky direct solar radiation has been applied to the glaciated regions of the river basin over the period 1961–2004. It was found that glacier runoff has increased in the last 44 years, especially in the 1990s when a two-thirds increase in river runoff was derived from the increase in glacier runoff caused by loss of ice mass in the entire Tuotuo River basin.     

缢蛏Sinonovacula constricta卵子发生的超微结构分析

本文描述了缢蛏卵子发生过程细胞学和超微结构的特征。在卵黄发生前期,卵母细胞胞质中存在着核质外排小体和大量的核糖体。在卵黄发生期,卵质中可见发达的内质网、环孔瓣状体和大量的线粒体及高尔基体等;卵核上有核膜泡和核膜小管的结构,并可观察到核孔数目大量增加。文中讨论了上述几种结构在卵子发生过程中的作用以及它们之间的关系。     

西沙群岛海产蓝藻的研究 Ⅴ

本文继续报道我国西沙群岛海产色球藻目 Chroococcales 的十种蓝藻,隶于三科六属，有八种是我国海产蓝藻的新记录,它们是:铜锈微囊藻 Microcystis aeruginosa Kuetzing,小形色球藻 Chroococcus minor ( Kuetzing) Naegeli,湖沼色球藻盐泽变种 C. limneticus Lemm.var. subsalsus Lemm,易变色球藻 C.varius A.Braun,膜状色球藻C. membraninus ( Meneghini) Naegeli,圆胞束球藻 Gomphasphaeria aponina Kuetzing,附钙管鞘藻 Hormathonema epilithicum Ercegovic 和透明拟丝藻 Johannesbaptistia pellucida ( Dickie) Taylor at Drouet。其中透明拟丝藻形态特征独特,这对于蓝藻类的研究,特别是研究从色球藻目进化到丝状蓝藻类的系统演化具有重要价值,早已为藻类学家们所重视。该种也是我国海产蓝藻属的新记录。 迄今为止,藻类学家们一致认为色球藻目是蓝藻门 Cyanophyta 中最原始、最低级的类群,其植物体最简单,大都是单细胞或简单的群体,而且都十分微小,常被肥厚的胶质鞘所包埋,这就使该目蓝藻能适应复杂的自然环境。所以,在地质学和古生物学的研究中它已引起许多学者的注意。有关该目海产种类的分类研究,我国仅有少数报道,这方面的工作还有待深入。     

西沙群岛海产蓝藻类的研究Ⅰ

我国西沙群岛海产蓝藻类的种类极为丰富。但迄今为止除黎尚豪报导过西沙群岛永兴岛的陆生习性的27种蓝藻外,尚未见过任何报告。本文为西沙群岛海产蓝藻类研究的第一部分。根据的材料是中国科学院海洋研究所1975年和1976年在西沙群岛采集的,计127号。本文仅报道11种,隶于颤藻科(Oscillatoriaceae)的6属,其中4种在我国为海产蓝藻的新记录。     

广州地区稻田甲烷排放及中国稻田甲烷排放的空间变化

１９９３年在广州地区采用中国科学院大气物理研究所研制的自动采集和分析系统测量了稻田甲烷的排放率，首次获得了占我国２０－２５％左右水稻收获面积的华南地区稻田甲烷排放特征值。从而宏观地使我国五个主要水稻生态区的甲烷排放率都有了实测资料。稻田甲烷排放率的季节变化主要与气温及灌溉水状态的变化的较大关系，日变化规律以下午出现极大为主。本实验田的甲烷排放率低。     

GNSS空间信号精度的研究分析

基于MGEX提供的产品,对当前全球卫星导航定位系统的空间信号精度分析与评估.结果表明:目前各系统的全球平均SISRE分别为GPS 0.6 m,Galileo 0.28 m,GLONASS 2.5 m,BDS-30.49 m,BDS-21.41 m;其中,Galileo最优,BDS-3次之,GPS稍低于BDS-3,另外,BDS-3 SISRE精度相对于BDS-2提升65％.     

新疆及邻区体波震级初步分析研究

依据《地震震级的规定（GB 17740—2017）》,分析了2009—2017年新疆地震台网所记录的新疆及邻区476次中深源地震事件,测定了13601个m_b（短周期体波震级）和12035个m_B（BB）（宽频带体波震级）的数据样本,回归分析m_b和m_B（BB）得到回归方程及量规函数,结果显示m_b和m_B（BB）相关系数为0.966,表明两者显著相关。因此,建议对于中强型中深源地震可以直接从原始速度型宽频带数字地震记录上测定长周期体波震级m_B（BB）,提高地震速报测定的速度和精度。通过震级偏差统计和台站场地响应计算,分析新疆地震台网中的XKR、HTA、ATS和KSZ等地震台站震级偏差较大的原因为砂岩、灰岩、砂土层等类型的台基放大了场地响应,说明台基类型对体波震级偏差的影响较大。与NEIC测定的体波震级对比时,发现新疆地震台网测定体波震级平均偏大0.42级,且偏差随着震源深度的增加有增大的趋势。