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.     

Overview of In - Situ Biodegradation and Enhancement

Microbial degradation technologies have been developed to restore ground water quality in aquifers polluted by organic contaminants effectively in recent years. However, in course of the degradation, the formation of biofilms in ground water remediation technology can be detrimental to the effectiveness of a ground water remediation project. Several alternatives are available to a remedial design engineer, such as Permeable Reactive Barriers (PRBs) and in -situ bioremediation, Hydrogen Releasing Compounds (HRCs) barrier, Oxygen Releasing Compounds (ORCs) barrier etc. which are efficient and cost- effective technologies. Excessive biomass formation renders a barrier ineffective in degrading the contaminants, Efforts are made to develop kinetics models which accurately determine bio - fouling and bio - filn formation and to control excessive biomass formation.     

ENVIRONMENTAL SUSTAINABILITY AND SCENARIOS OF URBANIZATION IN ARID AREA——A Case Study in Wuwei City of Gansu Province

1IN T R O D U C T IO N The western China isfacinggrowing problems of eco- logicaland economic development. The disparitiebse- tween the socio-economy of the western and eastern China were enlarging, and the major ecologicalprob- lems stilelvolve as past(H…     

Mobile Agent系统结构及其关键技术

Mobile Agent技术是一门新兴的技术,具有移动性、智能性和异步计算的特点,已成为计算机网络和分布式系统最具活力的发展方向.现对Mobile Agent的概念、系统结构及其关键技术进行了分析和探讨.     

An approximate method for evaluating the shear band thickness in saturated soils

A formula for the thickness of a shear band formed in saturated soils under a simple shear or a combined stress state has been proposed. It is shown that the shear band thickness is dependent on the pore pressure properties of the material and the dilatancy rate, but is independent of the details of the combined stress state. This is in accordance with some separate experimental observations. Copyright © 2004 John Wiley & Sons, Ltd.     

MATLAB在运用系统建模处理南海气象数据过程中应用

介绍了MATLAB语言特点和系统建模方法的基本理论.根据南海气象数据的实际建模处理过程,给出了建模的详细步骤及其MATLAB实现过程以及MATLABTM的主要实现程序.试验讨论和结果表明利用MATLAB语言可以方便地对南海气象数据用系统建模方法进行建模和处理,MATLAB在运用系统建模法处理南海气象数据方面具有明显的优越性.     

GPS/LEO掩星观测的变分同化技术

在简单介绍GPS/LEO掩星探测大气的发展历史和科学意义之后,详细阐述了反演的基本原理;分析了标准反演中存在的问题,并说明一维变分同化(1DVAR)在反演方法中的重要性;给出了一维变分同化中价值函数的求解,以及各种同化因子;简单介绍了对当前气象学中普遍使用的四维变分同化(4DVAR);重点讨论了各种同化方法,以及使用各种同化因子的优缺点。最后,通过CHAMP卫星的观测实例分析,验证了GPS数据在数值天气预报(NWP)中的作用,以及相对于标准反演法一维变分对气象要素的改进。     

Approach to Mountain Hazards in Tibet, China

Tibet is located at the southwest boundary of China. It is the main body of the Qinghai-Tibet Plateau, the highest and the youngest plateau in the world. Owing to complicated geology, Neo-tectonic movements, geomorphology, climate and plateau environment, various mountain hazards, such as debris flow, flash flood, landslide, collapse, snow avalanche and snow drifts, are widely distributed along the Jinsha River (the upper reaches of the Yangtze River), the Nu River and the Lancang River in the east, and the Yarlungzangbo River, the Pumqu River and the Poiqu River in the south and southeast of Tibet. The distribution area of mountain hazards in Tibet is about 589,000 km^2, 49.3% of its total territory. In comparison to other mountain regions in China, mountain hazards in Tibet break out unexpectedly with tremendously large scale and endanger the traffic lines, cities and towns, farmland, grassland, mountain environment, and make more dangers to the neighboring countries, such as Nepal, India, Myanmar and Bhutan. To mitigate mountain hazards, some suggestions are proposed in this paper, such as strengthening scientific research, enhancing joint studies, hazards mitigation planning, hazards warning and forecasting, controlling the most disastrous hazards and forbidding unreasonable human exploring activities in mountain areas.     

蛤蜊科3种贝类16SrRNA基因片段及ITS2核苷酸序列分析

利用PCR技术分别扩增连云港及启东沿海蛤蜊科的西施舌（Coelomactra antiquata）、中国蛤蜊（Mactra chinensis）和四角蛤蜊（Mactra veneriformis）3种双壳贝的16SrRNA基因片段和ITS2核苷酸序列．测序后用DNAstar软件分析了核苷酸差异。结果显示：三种贝类16SrRNA基因片段长度相同，均为306bp（去除引物）．核苷酸存在多态性。共有45个变异位点，54个核苷酸发生了变异。全部为碱基置换。西施舌与中国蛤蜊此片段核苷酸的同源性为88．9％．与四角蛤蜊的同源性为88．6％．中国蛤蜊与四角蛤蜊的同源性为90．6％。三种蛤蜊ITS2序列分别为390bp（西施舌）、441bp（四角蛤蜊）和466bp（中国蛤蜊）。存在长度多态性．ITS2核苷酸差异分析结果显示．西施舌与中国蛤蜊的同源性为70．9％-71．1％，西施舌与四角蛤蜊的为70．5％-71．0％。中国蛤蜊与四角蛤蜊的同源性为88．1％-88．8％。ITS2序列分析结果与16SrRNA基因片段分析结果一致．2种分子分析法均显示中国蛤蜊与四角蛤蜊的亲缘关系近。     

Optical monitoring of the quasar 4C 38.41

The results of a photometric monitoring of the quasar 4C 38.41, performed at the optical R and B bands in 2002 February–March, are presented. With a 60/90 cm Schmidt telescope at the Xinglong station of the National Astronomical Observatories of China, we observed the source exhibiting amplitude variations of up to 0.78 mag in both bands during the whole campaign. Intraday and even intranight variations are detected as well. A typical variability time-scale of about 36 d is derived from our 2-month observations at the optical bands, which is identical to that found at a radio wavelength of 92 cm, suggesting a common origin for the variations in 4C 38.41 from optical to radio bands.