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
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.
In recent years, many coal-producing countries have paid great attention to the land subsidence causedby coal cutting. In China, because of the dense population in coalfield areas, the land subsidence hazard is more seri-ous. After a brief analysis on the mechanism of land subsidence, this paper gives a comprehensive and systematical ac-count on all kinds of hazards caused by the land subsidence in China. The study shows that land subsidence has endan-gered land, buildings, traffic and communication lines, dykes and dams. It also causes damage to ecological and socialenvironment. In order to lessen the hazard of land subsidence, preventive measures should be taken to reduce the col-lapse amount, such as extraction with stowing, banded mining system, succession and coordination mining system, orhigh-pressure mudflow between rock strata. Measures of reinforcing or moving certain buildings should also be taken toreduce the destructive degree. In order to harness the subsidence land and bring them under control for fanning, mea-sures should be taken such as filling with spoil or fine breeze, excavating the deeper and covering the shallower land. 相似文献
Profiles of spectral lines emitted from an accretion ring around an object with strong gravitational field should be affected by Doppler shift, gravitational redshift, and deflection of light. Taking these effects into account, precise line profile of a Keplerian ring around a Kerr black hole for a distant observer is obtained by solving the kinetic equation of photons. 相似文献
1 INTRODUCTIONCompact Symmetric Objects (CSOs) are powerful and compact sources (overall size <1 bpc) with lobe emission on both sides of the central engine. The small size of these sourcesis almost certainly to be attributed to the youth of the sources themselves (ages < 104 yr) andnot due to a dense coallning medium (Readhead 1996). The unification scenario assumes thatCSOs evolve into compact steep spectrum (CSS) sotirces and then into Fanaroff-Riley type 11objects (Fanti 1995). … 相似文献