DocumentCode :
3156143
Title :
Research on the Evaluation Methods of Surface Water Quality Based on Spatial Data
Author :
Liu Yujia ; Liu Zhiming
Author_Institution :
Inst. of Atmos. Phys., Grad. Univ. of CAS, Beijing, China
fYear :
2010
fDate :
18-20 June 2010
Firstpage :
1
Lastpage :
4
Abstract :
Taking Second Songhua River as the study area, established the SQL Server-based spatial data warehouse. On this basis, using GIS, statistical data mining, visualized data mining and BP neural network to finish data preprocessing, build water quality assessment forecast model, and analyze spatial distributing of water quality. The results show that it is adapted to manage and analyze the spatial data if water resource was investigated by Spatial Data Mining(SDM), and it is easier to discover deep-seated information and rules from the mass of data; In the central and the southeast part of Second Songhua River, the water qualities are better than other places, most of them are class II or class III; The water qualities are poor in the northwest and the south part of Second Songhua River, they are class IV, class V or below class V; Every single evaluation index of water quality class shows that the changing trend from northwest to southeast are from high to low, and there are some special indexes have the trend of ascending in the southern part; According to comprehensive analyzing of spatial distribution character of Second Songhua River water quality, it has obvious area distribution, the mountainous area in the southern part of drainage basin has better water quality, however, it is poor in the northwest, both natural and human activities have influence on it.
Keywords :
backpropagation; data mining; data warehouses; geographic information systems; neural nets; reservoirs; rivers; water quality; BP neural network; GIS; SQL Server-based spatial data warehouse; Second Songhua River; area distribution; drainage basin; human activities; mountainous area; natural activities; spatial data mining; statistical data mining; surface water quality; visualized data mining; water quality assessment forecast model; water quality spatial distribution; Data mining; Data preprocessing; Data visualization; Data warehouses; Geographic Information Systems; Information analysis; Neural networks; Quality assessment; Rivers; Water resources;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th International Conference on
Conference_Location :
Chengdu
ISSN :
2151-7614
Print_ISBN :
978-1-4244-4712-1
Electronic_ISBN :
2151-7614
Type :
conf
DOI :
10.1109/ICBBE.2010.5518231
Filename :
5518231
Link To Document :
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