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 factors affecting permeability change under repeated mining of coal seams are important study aspects that need to be explored. This study combined various stress variation characteristics of protective seam mining and simplified the stress path of repeated mining in protective seam mines. Based on the results from the bespoke gas flow and displacement testing apparatus, seepage tests for simulated repetitive mining were carried out. The results simulated the actual behavior very well. With any drastic increase in the mining influence, the axial deviation stress in the stress path increased, and the greater the difference in coal permeability during the unloading and stress recovery stage, the more substantial the increase in permeability. The change in coal permeability was significantly influenced by the severity of simulated repeated mining cycles. When the mining stress exceeded a critical value, the permeability of the coal sample increased with the increase in the number of loading and unloading cycles, but the reverse was true when the mining stress was lower than the critical value. The effective sensitivity of seepage to the applied stress decreased with an increase in the number of stress cycles. With a decrease in the deviation stress, that is, with lower severity of mining influence, the effective sensitivity of coal seepage to stress gradually decreased.
Natural Resources Research - The coal fire area in the Wuda coalfield is divided into four parts based on the degree of burning and on surface characteristics: sub-area B is characterized by... 相似文献