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.     

Characteristics of Spatial and Temporal Variations of Monthly Mean Surface Air Temperature over Qinghai-Tibet Plateau

1 Introduction The Qinghai-Tibet Plateau, known as the highest plateau with the most complex topography in the world, covers an area of more than 200km2, with a mean elevation of more than 4000m a.s.l. (Ye and Gao, 1979). Surrounded by the Earth’s highest mountains, such as the Himalayas, Pamir, Kunlun Mountains, the plateau plays a significant role in climate change in China even in the world, thus attracted great attention of researchers. Up to now, many achievements have been gained by…     

用遗传算法解算病态方程

对应用遗传算法解决病态方程问题进行了探讨。利用拟合法而不是通过法方程求解参数，从而避免了法方程系数求逆，使病态方程的解答有了较好的结果。通过模拟计算并和其他方法进行比较，证明该方法是可行的和有效的。     

Magnetotelluric investigations for imaging electrical structure of Garhwal Himalayan corridor,Uttarakhand, India

Magnetotelluric investigations have been carried out in the Garhwal Himalayan corridor to delineate the electrical structure of the crust along a profile extending from Indo-Gangetic Plain to Higher Himalayan region in Uttarakhand, India. The profile passing through major Himalayan thrusts: Himalayan Frontal Thrust (HFF), Main Boundary Thrust (MBT) and Main Central Thrust (MCT), is nearly perpendicular to the regional geological strike. Data processing and impedance analysis indicate that out of 44 stations MT data recorded, only 27 stations data show in general, the validity of 2D assumption. The average geoelectric strike, N70°W, was estimated for the profile using tensor decomposition. 2D smooth geoelectrical model has been presented, which provides the electrical image of the shallow and deeper crustal structure. The major features of the model are (i) a low resistivity (<50Ωm), shallow feature interpreted as sediments of Siwalik and Indo-Gangetic Plain, (ii) highly resistive (> 1000Ωm) zone below the sediments at a depth of 6 km, interpreted as the top surface of the Indian plate, (iii) a low resistivity (< 10Ωm) below the depth of 6 km near MCT zone coincides with the intense micro-seismic activity in the region. The zone is interpreted as the partial melting or fluid phase at mid crustal depth. Sensitivity test indicates that the major features of the geoelectrical model are relevant and desired by the MT data.     

热带地区积云对流的长期变化特征

使用１９７９年１月至１９８４年１２月向外长波辐射（ＯＬＲ）资料，对热带地区积云对流的长期变化特征进行了研究。结果表明：热带地区积云对流活动存在显著的季节变化，冬季积云对流区主要是东西向，位于南印度洋和西太平洋的近赤道地区；夏季则北移至北印度洋和菲律宾附近的西太平洋地区。低纬地区积云对流活动存在明显的季节性位移，北印度洋地区的积云对流活动主要集中在５—１０月，７—８月位置最北；北半球热带西太平洋地区的积云对流活动则主要集中在６—１１月，８—９月位置最北。标准差分析表明，冬季北半球热带西太平洋、赤道中太平洋及热带印度洋东部地区积云对流的年际变化最明显。经验正交函数（ＥＯＦ）分析的主要空间型反映了赤道中太平洋、热带西太平洋、阿拉伯海和副热带西太平洋地区的积云对流活动存在一定的关系。结合遥相关计算还表明秋云对流存在４种遥相关型，即２种东西向偶极型涛动型、西太平洋型和北印度洋型。     

Total Lipid and Fatty Acid Classes in Decomposing Mangrove Leaves of <Emphasis Type="Italic">Bruguiera gymnorrhiza</Emphasis> and <Emphasis Type="Italic">Kandelia candel</Emphasis>: Significance with respect to Lipid Input

Changes in the concentration of total lipid and fatty acids (FAs) during the decomposition of mangrove leaves were investigated by field experiments using yellow leaves of Bruguiera gymnorrhiza (L.) Lamk. and Kandelia candel (L.) Druce, in order to quantify mangrove contribution to lipid and fatty acid inputs to marine sediments. Total lipid and total FA in the fresh (green and yellow) and decomposing leaves of both species were significantly higher during winter than summer. During decomposition, total lipid content and FA concentration, in particular branched chain fatty acids (BrFAs) and bacterial fatty acids (BFAs), increased to a maximum concentration in 45 days during winter and in 17 days during summer. Lipids were lost faster in K. candel leaf detritus than in B. gymnorrhiza leaf detritus in which >90% of the total lipid original weight was lost during the summer experiment and <60% during the winter experiment. The changes in the concentrations of total lipids and FAs in the decomposing leaves also indicate that mangrove leaves are significant sources of fatty acids and probably other lipid compounds to estuarine ecosystems and that tidal waters transport the lipids and FAs adsorbed to particulate matter from mangroves to adjacent estuarine sediments and the ocean.     

北太平洋海面异常风场的时空分布特征与海气热量交换

用北太平洋（含赤道太平洋区）１９４９～１９７９年的ＣＯＡＤＳ资料，通过矢量ＥＯＦ分解得到北太平洋异常风场的分布型及其时间变化规律。分析表明，ＥＯＦ１～５场与大气活动中心、海洋相互作用有联系，具有明显的天气学意义。此外，还讨论了ＥＯＦ异常风场间的转换及其季节性差异。     

Nitrogen forms and decomposition of organic carbon in the southern Bohai Sea core sediments

Study on form characteristics of nitrogen in marine sediments is the primary method to research its biogeochemical cycling and nitrogen form characteristics in core sediments can reflect the process and results of early diagenesis in a certain degree. In this paper, Sequential extraction process in natural grain size was used for studying the existent forms of nitrogen in five core sediments of the southern Bohai Sea for the first time. Nitrogen was divided into two parts -transferable and fixed based on whether it could be extracted by the reagent. Distributions and early diagenesis of transferable nitrogen forms in the southern Bohai Sea were researched integratedly. Results indicate that IEF - N and OSF-N are predominant forms in transferable part in the studied core sediments. Contents of different nitrogen forms vary differently with depth, and have different diagenesis process. Decomposition constant of organic nitrogen (ON) and OC are about 15.51 × 10~(-3)a~(-1)and 4.79× 10~(-3)a~(-1) respective     

低硼氢氧化镁的制备及热分解动力学参数

本文以水合氯化镁、工业氨水为原料制备出了低硼含量的纯度 >99%的氢氧化镁和氧化镁。利用差热 -热重 (DTA- TG)、X-射线衍射 (XRD)、透射电子显微镜 (TEM)等手段 ,对氢氧化镁的热稳定性、相结构、表观状态以及脱水动力学参数进行了分析。结果为 :氢氧化镁以束状纤维和片状粉末共存 ,在 6 2 3K基本完成脱水反应生成氧化镁 ,其脱水反应的表观活化能 E为135 .4 k J· mol-1,指前因子 A为 5 .15× 10 10 ,反应级数 n为 1.10     

基于EMD与神经网络的机械故障诊断技术

经验模式分解 (EMD)是分析非线性、非平稳信号的有力工具 ,它将信号分解为突出了原信号的不同时间尺度的局部特征信息的内在模函数 (IMF)分量。本文通过将各 IMF分量输入到 BP网络中进行训练学习和故障诊断 ,比直接输入原信号可以提高 BP网络对故障诊断的准确率 ,而且减少了训练时间。