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.     

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空间信息网格中远程空间连接查询的优化

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山地灾害研究的发展态势与任务

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关于角平分线法建立线状缓冲区算法的改进

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广东省特色农作物标准波谱数据库框架设计与示范应用

本研究着眼于广东省典型特色农作物的波谱，建立了波谱数据库，该库分为知识库、遥感实测波谱库、模型库、波谱分析和 荔枝园示范5大部分。 在波谱分析模块设计上提供了波谱数据查询、分析等功能，通过对荔枝园种植面积应用示范，给用户提供了极 大方便。     

内蒙古大青山地区寒武系与奥陶系之间的一个重要的层序界面

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Ion acoustic instability of HPT particles,FAC density,anomalous resistivity and parallel electric field in the auroral region

During the magnetic storm of 21st March 1990, the DE-1 spacecraft encountered the auroral region at high invariant latitude at altitudes ranging from a few thousand kilometers in the ionosphere to many earth radii in the magnetosphere. The magnetic field perturbations interpretable as field aligned current (FAC) layers and the electrostatic turbulence possibly due to electrostatic ion acoustic instability driven by these currents are shown. The critical drift velocity of Hot Plasma Torus (HPT) electrons and the growth rate of ion acoustic wave as a function of electron to ion temperature ratio (T _e/T_i) for low and high current densities and energy of HPT electrons are found out. The intense FAC destabilizes the ion acoustic wave and the resultant electrostatic turbulence creates an anomalous resistivity. The current driven resistivity produces parallel electric field and high power dissipation. The anomalous resistivityη, potential difference along the auroral field lines V_t|, intensity of electric field turbulenceE _t| and power produced per unit volumeP are computed. It is found that the change in westward magnetic perturbation increasesJ _t|, η, V_t|, E_t| andP. Hence HPT electrons are heated and accelerated due to power dissipation during magnetically active periods in the auroral region. Concerning, applications, such HPT electrons can be used in particle accelerators like electron ring accelerator, smokatron etc.     

螺旋坐标下的因子分解合成炮叠前深度偏移

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