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Peng Yue Fan Gao Boyi Shangguan Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274
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
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Khouloud Gader Ahlem Gara Marnik Vanclooster Slaheddine Khlifi Mohamed Slimani 《水文科学杂志》2020,65(8):1300-1315
ABSTRACT Predicting the impacts of climate change on water resources remains a challenging task and requires a good understanding of the dynamics of the forcing terms in the past. In this study, the variability of precipitation and drought patterns is studied over the Mediterranean catchment of the Medjerda in Tunisia based on an observed rainfall dataset collected at 41 raingauges during the period 1973–2012. The standardized precipitation index and the aridity index were used to characterize drought variability. Multivariate and geostatistical techniques were further employed to identify the spatial variability of annual rainfall. The results show that the Medjerda is marked by a significant spatio-temporal variability of drought, with varying extreme wet and dry events. Four regions with distinct rainfall regimes are identified by utilizing the K-means cluster analysis. A principal component analysis identifies the variables that are responsible for the relationships between precipitation and drought variability. 相似文献
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Metaheuristic techniques, which are based on ideas of Artificial Intelligence, are among the best methods for solving computationally the GPS surveying network problem. In this paper, the ant colony optimization metaheuristic, which is inspired by the behavior of real ant colonies, is developed to efficiently provide a general framework for optimizing GPS surveying networks. In this framework, a set of ants co-operate together using an indirect communication procedure to find good GPS observation schedules. A GPS surveying network can be defined as a set of stations, co-ordinated by a series of sessions formed by placing receivers on the stations. The problem is to search for the best order in which to observe these sessions to give the best schedule at minimum cost. Computational results obtained by applying the proposed technique on several networks, with known and unknown optimal schedules, prove the effectiveness of the proposed metaheuristic technique to solve the GPS surveying network problem. 相似文献
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Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide applications (drift). We present an improved geographic information system (GIS) and remote sensing method, the Landsat method, to estimate agricultural pesticide exposure through matching pesticide applications to crops classified from temporally concurrent Landsat satellite remote sensing images in California. The image classification method utilizes Normalized Difference Vegetation Index (NDVI) values in a combined maximum likelihood classification and per-field (using segments) approach. Pesticide exposure is estimated according to pesticide-treated crop fields intersecting 500 m buffers around geocoded locations (e.g., residences) in a GIS. Study results demonstrate that the Landsat method can improve GIS-based pesticide exposure estimation by matching more pesticide applications to crops (especially temporary crops) classified using temporally concurrent Landsat images compared to the standard method that relies on infrequently updated land use survey (LUS) crop data. The Landsat method can be used in epidemiologic studies to reconstruct past individual-level exposure to specific pesticides according to where individuals are located. 相似文献
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利用中分辨率光谱仪(MODIS)获得的气溶胶光学厚度(AOD)、细粒子比例(FMF)和臭氧检测仪(OMI)获得的气溶胶指数(AI)统计分析了2005—2014年我国华东地区气溶胶光学性质的时空分布特征,同时利用潜在源分析(PSCF)模型对我国华东地区AOD和AI的潜在源区进行分析。研究结果表明:华东地区的AOD、FMF和AI时空分布存在较大的差异,2005—2014年AOD和AI的平均值高值主要分布在华东地区北部,FMF的高值区则分布在华东南部地区;10 a间华东地区AOD呈现出先升高后降低的趋势,FMF波动幅度不明显,AI值有所上升;整个华东地区AOD的季节变化较为明显,春夏两季AOD明显高于秋冬两季。华东北部和中部地区夏季由于较高的相对湿度,AOD最大可达0.8以上。而在华东南部地区,夏季受到降水的影响,AOD维持在0.2~0.4之间。FMF季节变化趋势与AOD不同,夏季最大达到0.58,春季最小仅为0.26。AI平均值在冬季最大高达0.63,夏季最小,为0.27。PSCF分析显示华东地区AOD主要源区以局地排放为主,同时也存在由河南、湖北和湖南等周边省市近距离输送影响;AI以局地和北方远距离输送为主,同时也受到河南、湖北等周边省市近距离输送的影响。 相似文献
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城市新兴技术产业的演化路径比较分析——以长三角物联网产业为例 总被引：4，自引：1，他引：3
1990年代后期以来,经济地理学出现了演化思潮,一些新的概念为分析产业的空间演化提供了有效的理论工具。基于演化经济地理学中的路径依赖理论,以长三角地区的物联网产业为例,探讨了新兴产业兴起和演化过程的地方差异。重点选取无锡、上海、南京和杭州四个城市,采用多案例比较的方法,从产业发展的历史基础、集群状况、政府政策,以及主要动力机制等方面,分析了4个案例的异同点,并突出这些城市在区域经济和制度环境方面的差异。对产业演化机制的讨论还发现,技术基础、政策推动和偶然性事件是新兴技术产业初始发展阶段的主要动因。 相似文献
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本文回顾了气象预报技术发展的进程,人们不断通过创造性的智慧发挥,从传统的经验判断到依靠现代数学物理方法,实现通过模式方程组求解大气演变过程,取得了成功。但面对复杂的天气气候系统,许多非线性不确定问题仍难以解决。随着人工智能(AI)技术的发展,有希望助上一臂之力。文中介绍了一些初步设想和进展,在观测识别、数据处理、短时临近预报、模式参数化、预报结果集成分析等环节都有初步成果,进一步的研发、拓展值得期待。 相似文献
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针对传统知识驱动型滑坡灾害研究多依赖专业人员经验,具有主观性和不确定性的问题,该文提出了基于数据驱动滑坡致灾因子评价及危险性区划的方法。采用证据权模型,较好地平衡了滑坡危险性区划中准确性与高效性之间的矛盾,实现了较为精确的滑坡易发性及危险性区划;利用感知层、网络层、应用层的物联网技术,实现了高危险区滑坡点在线预警监测。3S技术支持下的滑坡危险性区划及监测实验结果表明:所用模型及监测技术不仅可以准确评价滑坡致灾因子权重及危险性区划,还能够精准、高效实现滑坡点实时监控预警。 相似文献
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