卫星观测到的我国闪电活动的时空分布特征

利用卫星上携带的闪电探测仪所获取的8年闪电资料,对我国闪电活动的空间分布、季节变化和日变化等特征进行了分析。结果表明：按照闪电活动的频繁程度在空间上可以将我国大致分为与太平洋海岸平行的4条闪电活动带：即近海区域、中部区域、西部区域和西部边境区域。其中,东南近海区域是我国闪电活动最频繁的地区,并向西北地区逐渐减弱,而西部地区则是我国闪电活动最弱的地区。我国的闪电活动呈现出明显的季节变化和日变化,平均而言,闪电活动主要集中在夏季(约占全年总闪电活动的68％),并以8月份最为活跃,春季次之(约占全年总闪电活动的24％),然后是秋季,冬季最弱,并在11月份出现闪电活动的最小值。在各个季节,随着纬度的减小闪电活动呈现出明显增大的趋势。闪电活动的日变化表明,我国的闪电活动绝大多数发生在午后至傍晚时分,这也与对流活动相对应,而少数地区发生在夜间。     

2006-2009年安徽省闪电活动时空特征分析

根据安徽省2006~2009年4年来10个地面测站的闪电定位数据资料,分析安徽省闪电活动的时空分布特点。从时间分布来看,闪电频次分布具有明显的季节性,春季闪电发生频次上升,夏季7月达最大值,秋季下降,至冬季达最小值。在闪电发生频次逐时分布中呈现出一定规律性,闪电多发生在1天中15:00-17:00,而在9:00发生频次最少。通过分析闪电空间密度分布图可以发现,南部闪电密度大于北部,原因是北部以平原为主,南部以山丘为主,且南北气候类型不同,北部为暖温带半湿润季风气候,南部为亚热带湿润季风气候,而且与北部相比南部经济发展快,城市高层建筑多,人口密度大,和空气污染相对严重。     

Hydraulic model tests for propagation of flow and sediment in floods due to breaking of a natural landslide dam during a mountainous torrent

During mountain torrents, large-magnitude floods may result from heavy rainfall and cause the breakage of landslide dams naturally formed by heavy rainfall, earthquakes, and so on. The characteristics of longitudinal spreading of clear water discharge and changes in flow depth must be clarified because the changes in peak depth have not yet been examined in steep-slope torrents and because there are few data on spreading of flash floods and related sedimentation in mountainous torrents. In the present study, experimental data were collected through hydraulic model tests over a rigid bed, and the spreading of water, fine sediment, bed load, and large boulders due to flooding are discussed assuming that flash flooding/debris flows occur in the upstream reach. The effects of changes in flow width, such as expansions and contractions in the flow width, as well as changes in meandering channels, sediment transportation, and spreading flow depth resulting from bores are examined using flume data for a steep-slope torrent. The data obtained in the present study reveal that fine sediment components are transported to the downstream reach if large-magnitude floods occur and that the spreading rate and peak lags of the fine sediment and water level indicate the occurrence of a flood in the upstream reach.     

前期土壤湿度和降雨对小流域山洪预警指标的影响评估

山洪灾害临界雨量预警指标受多种因素影响,尤其是前期土壤湿度状况和降雨变化。基于分布式水文模型和情景分析法确定安徽省岳西流域的山洪灾害临界雨量集合,评估前期土壤湿度状况、雨型和预警时段对临界雨量变化的影响。研究表明：① 中国山洪水文模拟系统在研究区内具有较好的适用性,率定期和验证期的平均径流深和洪峰流量相对误差均在15%以内,平均峰现时间误差在1h以内,平均Nash-Sutcliffe系数为0.79和0.77;② 60种情景模式下,池墩组村的临界雨量集合为141~528 mm;③ 前期土湿状况和雨型均显著影响临界雨量变化,随着土壤饱和率从0.20分别增加到0.50和0.80,临界雨量分别减少13.7%~16.2%和26.8%~31.8%;短历时预警时,临界雨量由大到小的相应雨型分别为减弱雨型、中间雨型和增强雨型。研究可为山洪灾害预报和早期预警提供理论支持,也为中国山洪灾害防治提供参考和借鉴。     

基于Flash开发多媒体电子地图的方法

通过分析解决基于Flash技术构建多媒体电子地图中的几个问题,具体说明包括地图数据转换和使用功能的实现方法。     

"测量学"教学动画软件研制

为更好地开展"测量学"教学,提高教学质量与效率,以Flash为开发工具,研制开发了"测量学"多媒体动画教学软件.这些动画软件能够生动形象地表现测量的原理与过程、展现仪器的操作使用细节,可激发学生的学习兴趣,丰富教学内容,能够增强教学效果,提高教学质量.     

基于Flash+XML+ASP技术的互动式网络地图构建

随着网络地图的迅猛发展,互动式网络地图兴运而生。笔者通过对现今较为成熟的网络地图发布技术及其实现方式进行比较研究,提出了利用Flash+XML+ASP技术构建互动式网络地图的新方法,并在此基础上详细介绍了采用此方法构建互动式网络地图的技术流程及具体实现方法,实现了真正意义上的"瘦客户端"的互动式网络地图。     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.