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.
Over the past 30 years, reclamation projects and related changes have impacted the hydrodynamics and sediment transport in the Bohai Sea. Three-dimensional tidal current models of the Bohai Sea and the Yellow Sea were constructed using the MIKE 3 model. We used a refined grid to simulate and analyze the effects of changes in coastline, depth, topography, reclamation, the Yellow River estuary, and coastal erosion on tidal systems, tide levels, tidal currents, residual currents, and tidal fluxes. The simulation results show that the relative change in the amplitude of the half-day tide is greater than that of the full-day tide. The changes in the tidal amplitudes of M2, S2, K1, and O1 caused by coastline changes accounted for 27.76–99.07% of the overall change in amplitude from 1987 to 2016, and water depth changes accounted for 0.93–72.24% of the overall change. The dominant factor driving coastline changes is reclamation, accounting for 99.55–99.91% of the amplitude changes in tidal waves, followed by coastal erosion, accounting for 0.05–0.40% of the tidal wave amplitude changes. The contribution of changes in the Yellow River estuary to tidal wave amplitude changes is small, accounting for 0.01–0.12% of the amplitude change factor. The change in the highest tide level (HTL) is mainly related to the amplitude change, and the correlation with the phase change is small. The dominant factor responsible for the change in the HTL is the tide amplitude change in M2, followed by S2, whereas the influence of the K1 and O1 tides on the change in the HTL is small. Reclamation resulted in a decrease in the vertical average maximum flow velocity (VVAM) in the Bohai Sea. Shallower water depths have led to an increase in the VVAM; deeper water depths have led to a decrease in the maximum flow velocity. The absolute value of the maximum flow velocity gradually decreases from the surface to the bottom, but the relative change value is basically constant. The changes in the tidal dynamics of the Bohai Sea are proportional to the degree of change in the coastline. The maximum and minimum changes in the tidal flux appear in Laizhou Bay (P-LZB) and Liaodong Bay (P-LDB), respectively. The changes in the tidal flux are related to the change in the area of the bay. Due to the reduced tidal flux, the water exchange capacity of the Bohai Sea has decreased, impacting the ecological environment of the Bohai Sea. Strictly controlling the scale of reclamation are important measures for reducing the decline in the water exchange capacity of the Bohai Sea and the deterioration of its ecological environment. 相似文献
