Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancem...Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancement (WaVE), a new framework and toolset that integrates enhanced geospatial analytics visualization (common operating picture) and decision support modular tools. WaVE enables users to: 1) dynamically generate on-the-fly, highly granular and interactive geovisual real-time and predictive flood maps that can be scaled down to show discharge, inundation, water velocity, and ancillary geomorphology and hydrology data from the national level to regional and local level;2) integrate data and model analysis results from multiple sources;3) utilize machine learning correlation indexing to interpolate streamflow proxy estimates for non-functioning streamgages and extrapolate discharge estimates for ungaged streams;and 4) have time-scaled drill-down visualization of real-time and forecasted flood events. Four case studies were conducted to test and validate WaVE under diverse conditions at national, regional and local levels. Results from these case studies highlight some of WaVE’s inherent strengths, limitations, and the need for further development. WaVE has the potential for being utilized on a wider basis at the local level as data become available and models are validated for converting satellite images and data records from remote sensing technologies into accurate streamflow estimates and higher resolution digital elevation models.展开更多
针对DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚类算法内存占用率较高的问题,文中将改进的DBSCAN聚类算法与Spark平台并行聚类计算理论相结合,对海量数据采用分而治之的办法进行聚类处理,大幅减小了算法...针对DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚类算法内存占用率较高的问题,文中将改进的DBSCAN聚类算法与Spark平台并行聚类计算理论相结合,对海量数据采用分而治之的办法进行聚类处理,大幅减小了算法对内存的占用率。实验仿真结果表明,所提出的并行计算方法能够有效缓解内存不足的问题,并且该方法也能够用来评价DBSCAN聚类算法在Hadoop平台下的聚类分析效果,还能对两种聚类方法进行对比分析,从而获得较好的计算性能;且其比在Hadoop平台上的计算加速度提高了24%左右,因此可以用以评价DBSCAN聚类算法在聚类处理方面的优劣。展开更多
文摘Riverine flood event situation awareness and emergency management decision support systems require accurate and scalable geoanalytic data at the local level. This paper introduces the Water-flow Visualization Enhancement (WaVE), a new framework and toolset that integrates enhanced geospatial analytics visualization (common operating picture) and decision support modular tools. WaVE enables users to: 1) dynamically generate on-the-fly, highly granular and interactive geovisual real-time and predictive flood maps that can be scaled down to show discharge, inundation, water velocity, and ancillary geomorphology and hydrology data from the national level to regional and local level;2) integrate data and model analysis results from multiple sources;3) utilize machine learning correlation indexing to interpolate streamflow proxy estimates for non-functioning streamgages and extrapolate discharge estimates for ungaged streams;and 4) have time-scaled drill-down visualization of real-time and forecasted flood events. Four case studies were conducted to test and validate WaVE under diverse conditions at national, regional and local levels. Results from these case studies highlight some of WaVE’s inherent strengths, limitations, and the need for further development. WaVE has the potential for being utilized on a wider basis at the local level as data become available and models are validated for converting satellite images and data records from remote sensing technologies into accurate streamflow estimates and higher resolution digital elevation models.
文摘针对DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚类算法内存占用率较高的问题,文中将改进的DBSCAN聚类算法与Spark平台并行聚类计算理论相结合,对海量数据采用分而治之的办法进行聚类处理,大幅减小了算法对内存的占用率。实验仿真结果表明,所提出的并行计算方法能够有效缓解内存不足的问题,并且该方法也能够用来评价DBSCAN聚类算法在Hadoop平台下的聚类分析效果,还能对两种聚类方法进行对比分析,从而获得较好的计算性能;且其比在Hadoop平台上的计算加速度提高了24%左右,因此可以用以评价DBSCAN聚类算法在聚类处理方面的优劣。
