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
The ecological series of soil animals under the broad-leaved and pine mixed forest in Darlidai Mountainwas studied. Seven sample plots were selected according to different altitude gradients, which belong to different vegeta-tion types. By investigating and analyzing soil animals in every sample plot it is found that there are 45 groups and 1956individuals, which axe involved in 3 phylums, 7 classes, 16 orders, respectively. The altitude is a key factor which af-fects ecological series of soil animals. Both the groups and individuals of soil animals increase with altitude increasingunder certain conditions, which contrastes with ordinary cases, resulting from special micro-climate in studied area. Thegroups and individuls of soil animals are the most under the broad-leaved and pine forest on the top of the mountain, andthe least under Picea-Abies forest in the foot of the mountain. 相似文献
As well known, the methods of remote sensing and Bowen Ratio for retrieving surface flux are based on energy balance closure; however, in most cases, surface energy observed in experiment is lack of closure. There are two main causes for this: one is from the errors of the observation devices and the differences of their observational scale; the other lies in the effect of horizontal advection on the surface flux measurement. Therefore, it is very important to estimate the effects of horizontal advection quantitatively. Based on the local advection theory and the surface experiment, a model has been proposed for correcting the effect of horizontal advection on surface flux measurement, in which the relationship between the fetch of the measurement and pixel size for remote sensed data was considered. By means of numerical simulations, the sensitivities of the main parameters in the model and the scaling problems of horizontal advection were analyzed. At last, by using the observational data acquired in agricultural field with relatively homogeneous surface, the model was validated.
Long-term measurement of carbon metabolism of old-growth forests is critical to predict their behaviors and to reduce the uncertainties of carbon accounting under changing climate. Eddy covariance technology was applied to investigate the long-term carbon exchange over a 200 year-old Chinese broad-leaved Korean pine mixed forest in the Changbai Mountains (128°28′E and 42°24′N, Jilin Province, P. R. China) since August 2002. On the data obtained with open-path eddy covariance system and CO2 profile measurement system from Jan. 2003 to Dec. 2004, this paper reports (i) annual and seasonal variation of FNEE, FGPP and RE; (ii) regulation of environmental factors on phase and amplitude of ecosystem CO2 uptake and release Corrections due to storage and friction velocity were applied to the eddy carbon flux.
LAI and soil temperature determined the seasonal and annual dynamics of FGPP and RE separately. VPD and air temperature regulated ecosystem photosynthesis at finer scales in growing seasons. Water condition at the root zone exerted a significant influence on ecosystem maintenance carbon metabolism of this forest in winter.
The forest was a net sink of atmospheric CO2 and sequestered −449 g C·m−2 during the study period; −278 and −171 gC·m−2 for 2003 and 2004 respectively. FGPP and FRE over 2003 and 2004 were −1332, −1294 g C·m−2. and 1054, 1124 g C·m−2 respectively. This study shows that old-growth forest can be a strong net carbon sink of atmospheric CO2.
There was significant seasonal and annual variation in carbon metabolism. In winter, there was weak photosynthesis while the ecosystem emitted CO2. Carbon exchanges were active in spring and fall but contributed little to carbon sequestration on an annual scale. The summer is the most significant season as far as ecosystem carbon balance is concerned. The 90 days of summer contributed 66.9, 68.9% of FGPP, and 60.4, 62.1% of RE of the entire year.
The optical flash accompanying GRB 990123 is believed to be powered by the reverse shock of a thin shell. With the best-fit physical parameters for GRB 990123 and the assumption that the parameters in the optical flash are the same as in the afterglow, we show that: 1) the shell is thick rather than thin, and we have provided the light curve for the thick shell case which coincides with the observation; 2) the theoretical peak flux of the optical flash accounts for only 3×10~-4 of the observed. In order to remove this discrepancy, the physical parameters, the electron energy and magnetic ratios, εe and εB, should be 0.61 and 0.39, which are very different from their values for the late afterglow. 相似文献
The authors analyzed the data collected in the Ecological Station Jiaozhou Bay from May 1991 to November 1994, including 12
seasonal investigations, to determine the characteristics, dynamic cycles and variation trends of the silicate in the bay.
The results indicated that the rivers around Jiaozhou Bay provided abundant supply of silicate to the bay. The silicate concentration
there depended on river flow variation. The horizontal variation of silicate concentration on the transect showed that the
silicate concentration decreased with distance from shorelines. The vertical variation of it showed that silicate sank and
deposited on the sea bottom by phytoplankton uptake and death, and zooplankton excretion. In this way, silicon would endlessly
be transferred from terrestrial sources to the sea bottom. The silicon took up by phytoplankton and by other biogeochemical
processes led to insufficient silicon supply for phytoplankton growth. In this paper, a 2D dynamic model of river flow versus
silicate concentration was established by which silicate concentrations of 0.028–0.062 μmol/L in seawater was yielded by inputting
certain seasonal unit river flows (m3/s), or in other words, the silicate supply rate; and when the unit river flow was set to zero, meaning no river input, the
silicate concentrations were between 0.05–0.69 μmol/L in the bay. In terms of the silicate supply rate, Jiaozhou Bay was divided
into three parts. The division shows a given river flow could generate several different silicon levels in corresponding regions,
so as to the silicon-limitation levels to the phytoplankton in these regions. Another dynamic model of river flow versus primary
production was set up by which the phytoplankton primary production of 5.21–15.55 (mgC/m2·d)/(m3/s) were obtained in our case at unit river flow values via silicate concentration or primary production conversion rate.
Similarly, the values of primary production of 121.98–195.33 (mgC/m2·d) were achieved at zero unit river flow condition. A primary production conversion rate reflects the sensitivity to silicon
depletion so as to different phytoplankton primary production and silicon requirements by different phytoplankton assemblages
in different marine areas. In addition, the authors differentiated two equations (Eqs. 1 and 2) in the models to obtain the
river flow variation that determines the silicate concentration variation, and in turn, the variation of primary production.
These results proved further that nutrient silicon is a limiting factor for phytoplankton growth.
This study was funded by NSFC (No. 40036010), and the Director's Fund of the Beihai Sea Monitoring Center, the State Oceanic
Administration. 相似文献
It has become clear in recent years that relativistic beaming is a good explanation for the BL Lac phenomenon. Of studies based on the relativistic beaming model of BL Lac objects, we note that the orientation of jet's axis to the line-of-sight is very small and, therefore, the observed flux emitted from a rapidly moving source is orders of magnitude higher than the flux in its rest-frame:Fobs = 3 + Fintr, where is the bulk relativistic Doppler factor. Then the observed apparent magnitudemv must be corrected for this effect. For our 39 samples, the corrected apparent magnitudemvcorr
and logZ have a good correlation. 相似文献
