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.     

Simulating methane emission from a Chinese rice field as influenced by fertilizer and water level

Natural and agricultural wetlands are considered to be the major sources of global atmospheric methane (CH₄). A one‐dimensional model was developed to simulate methane emission and used to examine the influence of various physical processes on the rate of methane emission. Three processes involved in the methane emission are implemented in the model: production, reoxidation and transport. Three transport pathways were considered: diffusion across water–air or soil–air interfaces, ebullition and diffusion through plants. These pathways are influenced by soil properties, plant growth, water‐table conditions, temperature and external inputs (e.g. fertilizer). The model was used to examine the seasonal variation of the methane emission at a rice field in Hunan, China, which was observed during a field experiment for consecutive (early and late) rice seasons in 1992. The observed seasonal variations of methane emission, and role of plants in transporting methane to the atmosphere, are captured by the model simulation. Further model applications were conducted to simulate effects of fertilizer and water‐level condition on the methane emission. The results indicate that unfermented organic fertilizer produces a higher methane emission rate than mineral fertilizer. The simulations with treatments of a deep‐water covering and constant moisture reduced the methane emission. The rice field study provides a framework for further development of the model towards simulations based on spatially distributed variables (e.g. water table, soil temperature and vegetation) at a regional scale. Copyright © 2003 John Wiley & Sons, Ltd.     

Glacier change and glacier runoff variation in the Tuotuo River basin,the source region of Yangtze River in western China

Glaciers in the Tuotuo River basin, western China, have been monitored in recent decades by applying topographical maps and high-resolution satellite images. Results indicate that most of glaciers in the Tuotuo River basin have retreated in the period from 1968/1971 to 2001/2002, and their shrinkage area is 3.2% of the total area in the late 1960s. To assess the influence of glacier runoff on river runoff, a modified degree–day model including potential clear-sky direct solar radiation has been applied to the glaciated regions of the river basin over the period 1961–2004. It was found that glacier runoff has increased in the last 44 years, especially in the 1990s when a two-thirds increase in river runoff was derived from the increase in glacier runoff caused by loss of ice mass in the entire Tuotuo River basin.     

缢蛏Sinonovacula constricta卵子发生的超微结构分析

本文描述了缢蛏卵子发生过程细胞学和超微结构的特征。在卵黄发生前期,卵母细胞胞质中存在着核质外排小体和大量的核糖体。在卵黄发生期,卵质中可见发达的内质网、环孔瓣状体和大量的线粒体及高尔基体等;卵核上有核膜泡和核膜小管的结构,并可观察到核孔数目大量增加。文中讨论了上述几种结构在卵子发生过程中的作用以及它们之间的关系。     

枣庄市台儿庄区小龚庄岩溶地下水水源地允许开采量计算评价

科学有效的地下水资源评价是可持续开发地下水资源的关键,其中地下水资源可开采量的评价是地下水资源评价的核心内容。该文在对枣庄市台儿庄区小龚庄水源地水文地质条件、岩溶塌陷发育情况等进行详细分析的基础上,采用开采试验法对小龚庄水源地岩溶地下水允许开采量进行了计算,计算结果为11354m~3/d。根据已发生岩溶塌陷情况,设定岩溶塌陷预警水位,利用非稳定流Theis公式计算了安全开采量范围在11187～13150m~3/d之间,对开采试验法计算结果进一步进行评价。根据评价结果最终确定以11354m~3/d作为小龚庄水源地的允许开采量是可靠的,可以保证水源地最大限度的开采地下水资源,同时又保证水源地运行时不易引发岩溶塌陷的环境地质问题。该方法为同类型水源地地下水允许开采量的计算评价提供了一定的借鉴。     

Interactive Effects of Ultraviolet Radiation and Dissolved Organic Carbon on Phytoplankton Growth and Photosynthesis in Sanya Bay,Northern South China Sea

Ocean Science Journal - The effects of a simulated climate change scenario, i.e., increased ultraviolet radiation (UVR) and dissolved organic carbon (DOC), on the growth and photosynthesis of...     

基于卷积神经网的CCTV视频中排水管道缺陷的智能检测

作为地下空间信息测绘工作的一个重要部分,基于排水管道内部测绘信息的管道缺陷检测越来越受到人们的重视。CCTV技术是一种广泛使用的排水管道内部测绘与缺陷检测技术。近些年基于卷积神经网的人工智能技术在图像识别中取得了巨大成功,受此启发,提出了一种基于卷积神经网络的排水管道缺陷的检测方法,以提高CCTV视频中的管道缺陷检测的自动化和智能化。试验证明了该方法的有效性,其在缺陷识别的准确率和召回率及识别速度上均满足了排水管道缺陷智能检测的需要;同时该方法也已经在深圳市的排水管道检测中得到广泛的应用。     

辽东湾海洋工程可靠性设计的海冰参数选取

海冰参数的合理取值是海洋工程海冰灾害风险评估的重要内容。利用1950—2018年的冰情等级(5个等级)数据,进行了1950—2018、1950—1990、1991—2018三种情景下的回归分析,确定了不同时期的冰情等级概率分布密度函数。利用鲅鱼圈雷达观测站2002—2017年的海冰现场实测资料,分别对鲅鱼圈附近海域一般冰厚、最大冰厚、最小冰厚进行概率分布拟合。基于上述概率分布结果,给出不同冰情等级的重现期,进而对海冰作业条件给出的设计参考值进行评价。结果表明:1990年以后2级、3级冰情重现期相对1990年之前变小,4级、5级冰情重现期相对1990年之前变大,规范给出重现期范围已不能代表辽东湾冬季海冰情况。本研究成果可为辽东湾海洋工程可靠性设计提供重要数据支撑。     

基于现场观测的辽东湾东岸海冰环境基本特征

全面获取气-冰-海环境特征数据已成为当前冬季现场观测的重要任务,用于准确、快速开展区域冰情评价预测,以满足海冰防灾减灾工作需求。基于辽东湾东岸鲅鱼圈雷达海冰观测站（15a）与温坨子附近海域（9a）的冬季观测数据,对辽东湾东岸的风、水温、海冰时空分布等环境基本特征进行了探讨。对鲅鱼圈站1月和2月4级风（5.5m·s-1）以上数据进行分析,发现偏北风自2016年以来呈现由N、NNE、NE向NE转变,偏南风自2016年以来主风向由SSW向WSW转变。对温坨子海冰观测站水温观测数据进行分析,发现水温表现出降温期、平稳期与回温期,降温期发生于12月至次年1月中旬,平稳期发生于1月下旬至2月中旬,回温期发生于2月下旬;当气温低于-10℃时,水温基本维持在-1.4℃附近,气温处于-10～-5℃时,水温基本分布于-1.4～-0.5℃区间,气温处于-5～5℃时,观测点水温基本分布于-1.4～3℃。对鲅鱼圈站与温坨子站能见范围内的逐日海冰分布面积比例进行分析,发现海冰面积呈现增大期、稳定期与减少期,鲅鱼圈站附近12月12日至次年1月9日前后为增大期（对应初冰期）,1月9日前后至2月8日前后为稳定期（对应盛...     

灰色系统理论在地下水水质预报中的应用

本文论述了“灰色系统”理论,模型建立步骤以及计算、检验、精度分析方法,并用郑州市地下水中氯离子浓度随时间变化作为实例进行了具体的预报。