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为使得气象信息管理更加标准化、制度化和科学化,做好气象服务的基础保障工作,针对气象业务、办公服务和管理要求等需求,以云MAS和微信公众服务平台的群发功能为基础,采用HTTP接口开发了面向气象业务系统的信息发送接口,该接口规范管理接入公众服务平台的方式,避免重复的开发功能和用户密码的管理混乱。建立气象信息管理数据库,基于C/S架构,采用前端控制模式以及C#和Python等开发平台,开发了气象信息监管模块,实现了信息监视、综合查询、统计分析、地址管理、参数配置、用户管理和关键字管理等功能。系统投入业务应用运行后,为信息发送至手机终端提供了方便快捷的对接方式和及时可靠的传输通道,提高了发送信息的可靠性和完整性,保障了管理信息的规范性和安全性。 相似文献
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利用2006—2009年江苏省闪电监测定位系统资料,对南京地区闪电活动特征进行了分析,发现闪电集中发生在6—8月的12—20时,其他各月闪电频数较低,这主要是由于南京夏季对流活动频繁,且在午后至傍晚的时间段内,气温偏高,较易产生强对流的雷暴天气等原因造成的.南京地区大部分区域的平均雷击大地密度值介于2~6次/(km2·a)之间,平均雷击大地密度极大值中心出现在江宁区,平均雷击强度值介于5~40 ka之间,南京长江大桥附近出现了平均雷击强度极大值的中心,最大值达75ka以上.地形地貌、下垫面性质、水汽条件等因素可能是导致上述特征的主因.所获得的闪电活动特征闪电参数在雷电防护、雷击风险评估等领域具有一定的应用价值 相似文献
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Data collection for landslide susceptibility modeling is often an inhibitive activity. This is one reason why for quite some time landslides have been described and modelled on the basis of spatially distributed values of landslide-related attributes. This paper presents landslide susceptibility analysis in the Klang Valley area, Malaysia, using back-propagation artificial neural network model. A landslide inventory map with a total of 398 landslide locations was constructed using the data from various sources. Out of 398 landslide locations, 318 (80%) of the data taken before the year 2004 was used for training the neural network model and the remaining 80 (20%) locations (post-2004 events) were used for the accuracy assessment purpose. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Eleven landslide occurrence related factors were selected as: slope angle, slope aspect, curvature, altitude, distance to roads, distance to rivers, lithology, distance to faults, soil type, landcover and the normalized difference vegetation index value. For calculating the weight of the relative importance of each factor to the landslide occurrence, an artificial neural network method was developed. Each thematic layer's weight was determined by the back-propagation training method and landslide susceptibility indices (LSI) were calculated using the trained back-propagation weights. To assess the factor effects, the weights were calculated three times, using all 11 factors in the first case, then recalculating after removal of those 4 factors that had the smallest weights, and thirdly after removal of the remaining 3 least influential factors. The effect of weights in landslide susceptibility was verified using the landslide location data. It is revealed that all factors have relatively positive effects on the landslide susceptibility maps in the study. The validation results showed sufficient agreement between the computed susceptibility maps and the existing data on landslide areas. The distribution of landslide susceptibility zones derived from ANN shows similar trends as those obtained by applying in GIS-based susceptibility procedures by the same authors (using the frequency ratio and logistic regression method) and indicates that ANN results are better than the earlier method. Among the three cases, the best accuracy (94%) was obtained in the case of the 7 factors weight, whereas 11 factors based weight showed the worst accuracy (91%). 相似文献
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