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
Methane content in coal seam is an essential parameter for the assessment of coalbed gas reserves and is a threat to underground coal mining activities. Compared with the adsorption-isotherm-based indirect method, the direct method by sampling methane-bearing coal seams is apparently more accurate for predicting coalbed methane content. However, the traditional sampling method by using an opened sample tube or collecting drill cuttings with air drilling operation would lead to serious loss of coalbed methane in the sampling process. The pressurized sampling method by employing mechanical-valve-based pressure corer is expected to reduce the loss of coalbed methane, whereas it usually results in failure due to the wear of the mechanical valve. Sampling of methane-bearing coal seams by freezing was proposed in this study, and the coalbed gas desorption characteristics under freezing temperature were studied to verify the feasibility of this method. Results show that low temperature does not only improve the adsorption velocity of the coalbed gas, but also extend the adsorption process and increase the total adsorbed gas. The total adsorbed methane gas increased linearly with decreasing temperature, which was considered to be attributed to the decreased Gibbs free energy and molecular average free path of the coalbed gas molecular caused by low temperature. In contrast, the desorption velocity and total desorbed gas are significantly deceased under lower temperatures. The process of desorption can be divided into three phases. Desorption velocity decreases linearly at the first phase, and then, it shows a slow decreases at the second phase. Finally, the velocity of desorption levels off to a constant value at the third phase. The desorbed coalbed gas shows a parabolic relation to temperature at each phase, and it increases with increasing temperature at the first phase, and then, it poses a declining trend with increasing temperature at the rest phases. The experimental results show that decreasing the system temperature can restrain desorption of coalbed methane effectively, and it is proven to be a feasible way of sampling methane-bearing coal seams.
A three-dimensional variational(3DVAR) data assimilation(DA) system is presented here based on a size-resolved sectional aerosol model, the Model for Simulating Aerosol Interactions and Chemistry(MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry(WRF-Chem) model. The use of this approach means that both gaseous pollutants such as SO_2, NO_2, CO, and O_3 as well as particulate matter(PM_(2.5), PM_(10)) observational data can be assimilated simultaneously.Two one-month parallel simulation experiments were conducted, one with the assimilation of surface hourly concentration observations of the above six pollutants released by the China National Environmental Monitoring Centre(CNEMC) and one without assimilation in order to verify the impact of assimilation on initial chemical fields and subsequent forecasts. Results show that, in the first place, use of the DA system can provide a more accurate model initial field. The root-mean-square error of PM_(2.5), PM_(10), SO_2, NO_2, CO, and O_3 mass concentrations in analysis field fell by 29.27 μg m~(-3)(53.5%), 34.5 μg m~(-3)(50.9%),30.36 μg m~(-3)(64.2%), 8.91 μg m~(-3)(39.5%), 0.46 mg m~(-3)(47.4%), and 15.11 μg m~(-3)(51.0%), respectively, compared to a background field without assimilation. At the same time, mean fraction error was reduced by 42.6%, 53.1%, 45.2%, 43.1%,69.9%, and 48.8%, respectively, while the correlation coefficient increased by 0.51, 0.55, 0.48, 0.38, 0.47, 0.65, respectively.Secondly, the results of this analysis reveal variable benefits from assimilation on different pollutants. DA significantly improves PM_(2.5), PM_(10), and CO forecasts leading to positive effects that last more than 48 h. The positive effects of DA on SO_2 and O_3 forecasts last up to 8 h but that remains relatively poor for NO_2 forecasts. Thirdly, the influence of assimilation varies in different areas. It is possible that the positive effects of DA on PM_(2.5) and PM_(10) forecasts can last more than 48 h across most regions of China. Indeed, DA significantly improves SO_2 forecasts within 48 h over north China, and much longer CO assimilation benefits(48 h) are found in most regions apart from north and east China and across the Sichuan Basin. DA is able to improve O_3 forecasts within 48 h across China with the exception of southwest and northwest regions and the O_3 DA benefits in southern China are more evident, while from a spatial distribution perspective, NO_2 DA benefits remain relatively poor. 相似文献
Natural Hazards - Many dams have been constructed around the world. Compared with the potential losses of life and economy, the environmental impacts caused by dam breach were less analyzed. As a... 相似文献
