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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大同—阳高Ms6.1地震前兆综合加权信息熵的变化

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对青藏东北缘现今块体划分、运动及变形的初步研究

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抽油机悬点投影测量仪

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Influence of the Choice of Core-Envelope Transition Point on the Binary Merger of Two Main-sequence Components

We have studied the influence of different choices of core-envelope transition point on the final merger of contact binaries with two main-sequence components. A binary of 1.00 + 0.90M⊙ with an initial orbital period of 0.35d is examined. The mass fraction of the primary mixed with the matter of the secondary, qmix, determined by the chosen core-envelope transition point, ranges from 0.04 to 1.00 in our analysis. If as qmix< 0.8, none of the     

Geochemical survey of thermal system in Northeast China and their geothermometric characters

The chemical composition of several thermal springs around Changbaishan area has been investigated. Cenozoic basaltic rocks are widely distributed in Northeast China and geothermal characteristics have been described. About one hundred hot springs exist around Changhaishan Volcano at the border between China and D.P.R.Korea with high temperature about 82~C. The pH values of the spring water range from 6.9 to 7.1 and the total flow rate is about 4.8 L/sec. The chemical composition of the thermal springs is sodium carbonate; the high-mineral contents of thermal water are believed to have medicinal properties. Bathhouses are already built along the hot springs to take the advantage of the supposed healing properties. The high quality of those hot springs is believed to be utilitized for mineral water. The chemical equilibrium temperatures were estimated at about 160℃ based on the Na-K-Ca geochemical thermometer.     

地壳对海洋潮汐的响应

应用三维动态有限元方法研究了中国北部地区的地壳对邻近的渤海与黄海海平面变化的响应。虽然此应力场过于微弱不足以引发地震，但发现应力集中的位置及应力场变化较大的位置恰好与某些现代地震的震中一致。这一结果表明研究地壳对广泛分布的载荷的响应对研究区域地震构造是有帮助的。     

青藏块体东北缘断层形变与中强地震

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Observational evidence for solar coronal decimetric radio pulsations with very short periods

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