The Research of Urban Waterlogging-Dragging
Decision Support System Based on GIS
Li Jun, Bian Fuling, Hu Zifeng
Informatics School of Wuhan Techinical University of Surveying and Mapping
Luoyu road 129#, Wuhan, China, 430079
Tel: (86)-(27)-87646560 Fax: (86)-(27)-87646560
E-mail:1j_2000@263.net
Abstract: this paper studies urban waterlogging-draining decision support system on basis of the application techniques, it employs 4D and visualization techniques to organize all data that needed, and to reach the functions of the system. At last, it gives a real case: Ezhou Waterlogging-Draining decision Support System.
1. Introduction
China is a country that suffers many natural disasters, each year the losses on this item occupies 3 to 5 percent of National Gross Product, flood and waterlogging are major factors in natural disasters, and flood always accompanies waterlogging. In recent years, the losses caused by flood and waterlogging increased steadily. In some areas, the waterlogging expense is even higher than flood expense. In China, almost half of the National Gross Product distributes in relatively lower areas such as seaside, riverside, lakeside etc. when flood seasons comes, heavy rainfall down in very short time; sometimes during this period it could occupy 80 percent rainfall of the whole year. The water level around the city keeps very high and created great pressure for city to discharge it, this could lead to urban waterlogging when water takes accumulating is such areas. With speeding urbanization, the likelihood of waterlogging caused by frequent rainstorms is getting bigger. Thus, to build urban waterlogging-draining decision support system is necessary, it could improve the forecasting and evaluating work for urban waterlogging, to supply scientific proof for waterlogging-draining decision and waterlogging-preventing plan decision. Through scientific analysis it can be relieve the losses in best way and achieve great economic and social value.
2. Data Structure and analysis Result Display of Urban waterlog-Draining Decision Support System.
2.1 Techniques Being Employed
Urban Waterlogging-Draining decision Support system concerns a huge amount of basic Information, including satellite images, aerial photographs, traditional topographic maps etc. These raster data and vector data, according to their different use, different layer and different class, through means of digital photogrammetry, are processed to get digital surveying information products based on 4D. 4D include Digital Elevation Model (DEM), Digital Othophoto Quadrangles (DOQ), Digital Raster Graphics (DRG) and Digital Line Graphs (DLG). 4D technique can complete information merging of raster and vector data, overlap multi-layer information of different structure. After correction and rectification of raster data, then matching with vector data, users can analyze the overlay graph of raster-vector data comprehensively and extract information being interested in.
As the key point for Decision Support System is assistant decision, an important factor that influences the system analysis outcomes to the users. Using 2D or 3D figures to display features and areas affected by waterlogging, using different means (zoom in, zoom out, pan and flash etc.) to highlight interesting geographic features, all these are needed to be done based on visualization technique. As elevation information's been stored as an attribute value, so basically this data does not has 3D GIS data structure. Through it will encounter some problems in making 3D analysis based on the data structure of DEM, the terrain information can completely satisfy the analyzing operations on rainfall-water level model and water depth distribution model. In application, it adopts 2D analysis combining with simulation display.
2.2 Data Organizing
Raster data: Dem, DOQ, DRG database on the whole area.
Vector data: multi-theme DLG database, storing spatial data such as boundary, stream system, traffic, pipe lines and water conservancy facilities etc.
Non-spatial infrastructure information database: the information about real time meteorological references, historical rainfall recordation, historical water level recordation, historical water level recordation, draining facility descriptions, policies and statutes on waterlogging-draining, traditional working experiences against waterlogging.
The system designer can choose
the type of database for spatial and non-spatial and non-spatial data: Distributed Database or Centralized Database, Relational Database or Object-Oriented Database.
3. Major Targeting Objectives of Waterlogging-draining Decision Support System
3.1 Comprehensive Information Query
In order to assure the reliability of system analyzing results, it's necessary to assure all information being input into the system are correct and well organized. These data consist of multi-region, multi-theme files such as DEM, DOQ, DGR, DLG, and other information including real time rainfall, water level and location distribution of waterlogging-draining facilities etc.
More accurate outcomes can be reached by querying users if the information is renewed frequently and correctly. Tables, word descriptions and vivid figures (histograms, pie chart) are effective means to tell users what they need. This is another visualization on geographic information.
3.2 Disaster Forecasting and real time Disaster Analyzing
Disaster Forecasting and real time Disaster Analyzing are prerequisites and foundations for assistant decision. During disaster weather, system receives rainfall data, sends it to analyzing module, then system will evaluate regions suffering waterlogging on considering the terrain of the region. On the basis of rainfall-water level model and digital elevation model, it supplies maps on waterlogged area and interesting features inside it.
According to rainfall forecasting, system uses figures to vividly display the range of possible waterlogging areas and water depth distribution maps. After vector-raster data matching, system overlaps DOQ and DLG of specific theme to get numerical statistic on a theme, such as urban retaining water capacity, waterlogging losses evaluation, all influenced industrial centers etc.
