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

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

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.  相似文献   

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

X. B. Lu  Wang Shuyun  Wang Yihua  Peng Cui 《国际地质力学数值与分析法杂志》2004,28(15):1533-1541

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.  相似文献   

Population synthesis for symbiotic stars with white dwarf accretors     

Guoliang Lü  L. Yungelson  Z. Han 《Monthly notices of the Royal Astronomical Society》2006,372(3):1389-1406

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GPS/LEO掩星观测的变分同化技术   总被引：6，自引：0，他引：6  

刘敏  郭鹏 《天文学进展》2006,24(1):27-42

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。  相似文献   

Approach to Mountain Hazards in Tibet, China   总被引：1，自引：1，他引：0  

MADongtao TUJianjun CUIPeng LURuren 《山地科学学报》2004,1(2):143-154

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.  相似文献   

桐柏─大别山北麓金矿带地质物化探找矿模型     

韩存强  徐大富  陈录 《河南国土资源》1995,(1)

从桐柏─大别山成矿带区域地质、地球物理、地球化学特征的研究入手，分析与金成矿有关的地层、构造、岩浆岩等控矿因素的地球物理场及地球化学特征；建立区域金矿找矿模型。  相似文献   

洛坝铅锌矿床的构造演化分析     

韩崇德 《地球科学与环境学报》1992,(4)

洛坝铅锌矿床位于黄诸关韧脆性变形带所波及的范围内,断裂、褶皱、劈理、节理等形态构造类型均很发育,经对小型构造的统计及应力场分析证明,矿区变形主要是在海西—印支期长期存在的南北向古应力场持续作用下形成的。  相似文献   

描述地壳垂直运动过程的一种函数形式     

杨国华  巩曰沐  卢景忠  韩月萍 《大地测量与地球动力学》1995,(1)

本文在已有数据处理方法的基础上，利用近代数值逼近理论，给出了从时空域角度描述地壳垂直运动过程的一种具体的函数解析形式。最后给出了一个实际算例。  相似文献   

Optical monitoring of the quasar 4C 38.41     

B. Peng  J. Wu  X. Zhou 《Monthly notices of the Royal Astronomical Society》2003,346(2):483-488

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.  相似文献   

SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK     

WANGXie-kang HUANGEr CUIPeng 《中国地理科学(英文版)》2003,13(3):262-266

Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.  相似文献