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 acquisition of spatial-temporal information of frozen soil is fundamental for the study of frozen soil dynamics and its feedback to climate change in cold regions. With advancement of remote sensing and better understanding of frozen soil dynamics, discrimination of freeze and thaw status of surface soil based on passive microwave remote sensing and numerical simulation of frozen soil processes under water and heat transfer principles provides valuable means for regional and global frozen soil dynamic monitoring and systematic spatial-temporal responses to global change. However, as an important data source of frozen soil processes, remotely sensed information has not yet been fully utilized in the numerical simulation of frozen soil processes. Although great progress has been made in remote sensing and frozen soil physics, yet few frozen soil research has been done on the application of remotely sensed information in association with the numerical model for frozen soil process studies. In the present study, a distributed numerical model for frozen soil dynamic studies based on coupled water-heat transferring theory in association with remotely sensed frozen soil datasets was developed. In order to reduce the uncertainty of the simulation, the remotely sensed frozen soil information was used to monitor and modify relevant parameters in the process of model simulation. The remotely sensed information and numerically simulated spatial-temporal frozen soil processes were validated by in-situ field observations in cold regions near the town of Naqu on the East-Central Tibetan Plateau. The results suggest that the overall accuracy of the algorithm for discriminating freeze and thaw status of surface soil based on passive microwave remote sensing was more than 95%. These results provided an accurate initial freeze and thaw status of surface soil for coupling and calibrating the numerical model of this study. The numerically simulated frozen soil processes demonstrated good performance of the distributed numerical model based on the coupled water-heat transferring theory. The relatively larger uncertainties of the numerical model were found in alternating periods between freezing and thawing of surface soil. The average accuracy increased by about 5% after integrating remotely sensed information on the surface soil. The simulation accuracy was significantly improved, especially in transition periods between freezing and thawing of the surface soil. 相似文献
Tsunamis are traveling waves which are characterized by long wavelengths and large amplitudes close to the shore. Due to the transformation of tsunamis, undular bores have been frequently observed in the coastal zone and can be viewed as a sequence of solitary waves with different wave heights and different separation distances among them. In this article, transient harbor oscillations induced by incident successive solitary waves are first investigated. The transient oscillations are simulated by a fully nonlinear Boussinesq model, FUNWAVE-TVD. The incident successive solitary waves include double solitary waves and triple solitary waves. This paper mainly focuses on the effects of different waveform parameters of the incident successive solitary waves on the relative wave energy distribution inside the harbor. These wave parameters include the incident wave height, the relative separation distance between adjacent crests, and the number of elementary solitary waves in the incident wave train. The relative separation distance between adjacent crests is defined as the ratio of the distance between adjacent crests in the incident wave train to the effective wavelength of the single solitary wave. Maximum oscillations inside the harbor excited by various incident waves are also discussed. For comparison, the transient oscillation excited by the single solitary wave is also considered. The harbor used in this paper is assumed to be long and narrow and has constant depth; the free surface movement inside the harbor is essentially one-dimensional. This study reveals that, for the given harbor and for the variation ranges of all the waveform parameters of the incident successive solitary waves studied in this paper, the larger incident wave heights and the smaller number of elementary solitary waves in the incident tsunami lead to a more uniform relative wave energy distribution inside the harbor. For the successive solitary waves, the larger relative separation distance between adjacent crests can cause more obvious fluctuations of the relative wave energy distribution over different resonant modes. When the wave height of the elementary solitary wave in the successive solitary waves equals to that of the single solitary wave and the relative separation distance between adjacent crests is equal to or greater than 0.6, the maximum oscillation inside the harbor induced by the successive solitary waves is almost identical to that excited by the single solitary wave. 相似文献
It is important to be fully aware of the dynamic characteristics of saturated soft clays under complex loading conditions in practice. In this paper, a series of undrained tests for soft clay consolidated with different initial major principal stress direction ξ were conducted by a hollow cylinder apparatus (HCA). The clay samples were subjected to pure principal stress rotation as the magnitudes of the mean total stress p, intermediate principal stress coefficient b, and deviator stress q were all maintained constant. The influences of intermediate principal stress coefficient and initial major principal stress direction on the variation of strain components, generation of pore water pressure, cyclic degradation and non-coaxiality were investigated. The experimental observations indicated that the strain components of specimen were affected by both intermediate principal stress coefficient and initial major principal stress direction. The generation of the pore water pressure was significantly influenced by intermediate principal stress coefficient. However, the generation of pore water pressure was merely influenced by initial major principal stress direction when b?=?0.5. It was also noted that the torsional stress–strain relationships were affected by the number of cycles, and the effect of intermediate principal stress coefficient and initial major principal stress direction on the torsional stress–strain loops were also significant. Stiffness degradation occur under pure principal stress rotation. Anisotropic behavior resulting from the process of inclined consolidation have considerable effects on the strain components and non-coaxial behavior of soft clay.
The lack of sufficient direct observation data of typhoon fine structure is the main bottleneck that restricts the further development of typhoon discipline and forecasting. This paper briefly introduced the basic information of the National Key R&D Program of China, entitled “Experiment on Coordinated Observation of Offshore Typhoon in China”, which started in early 2019. Firstly, the importance and necessity of the program around the national needs on typhoon-related disaster reduction and prevention were explained. Then, the coordinated observation difficulties and frontiers in the current typhoon discipline situation from the development and improvement of the physical mechanism and key forecasting technologies were shown. The overview of the direct observation instrument and platform, the field campaign and the parameterization techniques related to physical process in typhoon numerical modeling was provided. Finally, the key scientific and technical issues and main research contents of the program were given. 相似文献
气候是控制柴达木盆地盐类沉积的主要因素之一,但是其作用机制尚待明确。作者以柴达木盆地察汗斯拉图盐湖的3个含盐剖面为研究对象,采用多接收电感耦合等离子质谱(MC-ICP-MS)铀系测年测定其沉积时代,并通过X射线粉晶衍射(XRD)分析测定其盐类矿物种类。铀系测年显示D18剖面石盐和芒硝层的沉积时代为13.1±2.0 ka BP~15.9±2.5 ka BP,其中芒硝沉积年代属于末次冰期MIS2晚期;MXK2剖面芒硝层的沉积时代分别为131.7±39.5 ka BP和158.3±10.8 ka BP,D12剖面芒硝层的沉积时代分别为166.6±20.2 ka BP和198.0±20.6 ka BP,可以对应于倒数第二次冰期MIS6。XRD分析确定了3个剖面的盐类矿物主要为芒硝、石盐和石膏。综合多个盐湖晚第四纪成盐数据,本文认为倒数第二次冰期MIS6和末次冰期MIS2是柴达木盆地晚第四纪重要的成盐期,冰期的冷干气候有利于石盐和芒硝等盐类沉积。柴达木盆地"冰期成盐"的根本原因,是由于冰期环境下盆地周边山体冰川规模的扩张以及干冷的冰期气候,共同造成了盐湖补给水量的减少。此外,晚第四纪MIS6和MIS2的冰期降温也是导致盆地中冷相盐类沉积的直接原因。 相似文献
Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone. 相似文献