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Accurate river channel and flood plain representation plays vital part in flood risk analysis. Terrain models such as TINs and DEMs are normally used to represent floodplains. But unfortunately finding a terrain model with a high density of stream channel elevation points that are sufficient for hydraulic modeling is not a easy task. However for years engineers and researchers have developed a high-resolution cross-section data for hydraulic modeling from field surveys, photogrametries and topographic maps. This research presented here introduces the procedures for creating integrated multiresolution TIN (ITIN) models for highresolution flood plain representation for flood risk analysis. The high-resolution river channel geometric data stored in HEC-6 hydraulic model and low-resolution flood plain data in the form of DEM created in Arc View GIS 3.2a were integrated by resolving the coordinate incompatibility in the both system. An integration procedure (ArcView extention) namely AVHEC6.avx has been developed between HEC-6 Hydraulic Model and ArcView GIS 3.2a to visualize model outputs in a more presentable manner through 3D capabilities of GIS.

In this paper, we propose a “center” type of electric vehicle charging dynamic route planning service system architecture, and the architecture of the server-side data management technology and services were studied, through GIS technology, the optimalal location of each electric vehicle charging station sultimately comestrue, which ensures the coordination and harm onization of electric vehicles, charging stations and users, saves the time for travel and charging, and improves operating efficiency. Simultaneously, taking full account of the requirements of power grid, select electric vehicle charging station, use the resources of electric vehicle charging stations rationally, to ensure the safe operation of grid.

Facing with the constantly overloaded operation of the life support system on earth currently, the ecological environmental problems become more and more serious on a global scale, and the ecological security of the geographic space attracts extensive attentions increasingly. Based on GIS and spacial ecological condition, this paper builds the ecological security evaluation index system, sets up the evaluation criterion, designs the space ecological security evaluation model further, and analyzes the evaluation results of the province regions in China from 1998 to 2012, so as to have the definite strategy for promoting the spacial ecological security in the province region.

GIS is widely used in grid for its excellent performance. In the practical operation, besides the accidents caused by discharge faults, there are also a lot of accidents which are induced by mechanical faults. The electrical detection methods are mainly about discharge faults, there's lack of methods about mechanical faults. The field measurement of 110 kV GIS, especially the part of bus, is carried out using the GIS vibration test system in this paper. The results show that the vibration of bus near the part with contact faults is strongest, while the bus vibration is generally weak. The results provide data support for the on-site testing of GIS vibration and fault diagnosis of mechanical faults.

The quantitative estimation of forest biomass provides important reference information for global carbon storage and carbon cycle research. This study based on the four periods TM remote sensing data of 1970s, 1980s, 1990s and early 21st century and forest resources inventory sample data during the same period, with ArcGIS analyzed the forest biomass spatial distribution characteristics of Lesser Khingan Range, and acquired the forest biomass distribution law with elevation and slope change, the study results are important to maintain the ecological balance of the three northeast provinces of China. Research results shows that: in this study area with the increasing of elevation the forest biomass and distribution have a lesser proportion, the sequence of forest biomass distribution with slope change of each period from big to small is: slope class 1 > slope class 2 > slope class 3 > slope class 4 > slope class 5 = slope class 6.