A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

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.     

Drought assessment in a south Mediterranean transboundary catchment

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.     

An Artificial Intelligent design for GPS Surveying Networks

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.     

Linking pesticides and human health: A geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure

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.     

华东地区气溶胶分布和变化特征研究

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。     

Human genome behaviour: a powerful mechanism for optimizing the use of space technology in surveying networks design

An approach based on the behaviour of human genome is developed to efficiently provide a general framework for optimizing the use of space technology in surveying networks design. The developed approach attempts to apply the successful self-organizing principles based upon the biological evolution to artificial intelligence. It mimics the phenomena of natural selection observed in nature to achieve its goals by continuously adopting a population of candidate solutions and improving its performance over successive generations. The goal of adaptation is to find the best solution that optimizes the design of a surveying network based on the use of satellite observations. This 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 for observing these sessions to give the best observation schedule at minimum cost. The obtained results prove the effectiveness of the developed technique in term of solution quality and computational efforts. The Corresponding Author in formed me that his address is likely to change soon. Please contact me, leick@spatial.maine.edu for the latest on this change.     

Role of the Rossiter-McLaughlin effect in the study of close binaries

This paper is devoted to binary stars belonging to the class of eclipsing-variable systems.Photometric and spectroscopic analysis of eclipses allows us to determine geometric parameters of the orbit and physical characteristics of stellar components as well as inclinations of stellar equators to the orbital plane. Estimations of inclinations can be obtained from measurement of the Rossiter-McLaughlin effect, which is discussed using examples of some eccentric binaries with an anomalous apsidal effect. Our task is to find the complete spectrum of solutions of the equation of apsidal motion, depending on the inclinations of the polar axes of the components to the orbital one for these systems, based on their individual spectroscopic and photometric observational data. The matrix of solutions allows us to select those pairs of polar inclinations that provide agreement with the observational apsidal period.     

“天地图·宁夏”图面整理浅析

在全国“天地图”省级节点建设工作中,位于西北的宁夏回族自治区也是重要一环,文章阐述了在“天地图·宁夏”项目成果整理阶段,如何在适当的软硬件环境下,根据相应的数据基础,运用AdobeIllustrator软件,进行图面配置的方法,特别是配图时必须解决好的几个常见问题,如：调整道路的叠放顺序、POI符号及其注记处理、道路编码整理规范、水系注记的表达方式、地物矛盾的处理原则等,以期通过实践摸索,反复比较、改进,找到配图表达的最优方案,以期达到良好效果。     

X射线极光强度与亚暴指数AE的相关关系

研究了Polar卫星的极区电离层X射线成像仪（PIXIE）得到的极光X射线成像强度AI（Auroral Intensity）与磁层亚暴指数的相关关系.本文发现,在所选取的1997年至2001年的部分数据中,从完整的X射线图像得到的极光X射线总强度和AE指数有很好的线性相关关系,在全部83组数据中有566％的数据的线性相关系数都在060以上（相关系数最大为097）.所以本文认为极光X射线总强度可以作为新的磁层亚暴卫星实时监测指数.     

基于环境因子AI-GIS方法的天然滑坡危险性预测--以香港大屿山岛为例

利用人工神经元网络(AI)及地理信息系统(GIS)技术,将多元空间信息分析与非线性理论相结合,建立了基于环境因子的区域滑坡非线性预测模型.选择天然滑坡比较发育的香港大屿山岛中部作为试验研究区,定量分析了滑坡与各种环境因子的空间关系,进行了因子筛选,据此对区域天然滑坡进行了危险性分区.结果表明,该方法可获取较高的预测精度,是完全可行的.该方法的应用将为区域滑坡危险性区划及滑坡灾害的防治提供重要依据.