Title :
The intelligent space-time prediction and analysis of the 3D loess slope geological disaster based on GIS
Author :
Zheng, Kun ; Yang, Yang
Author_Institution :
Fac. of Inf. Eng., China Univ. of Geosci., Wuhan, China
Abstract :
Because of combined effects of climate, soil conditions, earthquake and wider area of loess in the loess region, the landslide disasters occur frequently in China´s northwest. The authors propose an intelligent space-time prediction and analysis method which can be applied widely. The method can achieve 3D expression of prediction and analysis of landslide geological disasters, and it can provide a visual 3D information platform for the results and methods. This paper provides technical methods and theoretical basis to reduce and prevent the landslide geological disasters based on intelligent space-time predicting and analysis method. The main work of this paper is as follows: 1) intelligent modeling; 2) space-time model establishment. It includes two parts: the first part is forecasting 3D space. The program quantitatively calculated 3D stability modulus by taking advantage of GIS-based 3D cell model, and then searched out the most unfavorable slip surface by genetic algorithm. The second part is time predicting; 3) intelligent knowledge base construction. Using artificial intelligence technology to analyze, extract landslide factors and estimate factors´ weights, the authors construct an intelligent knowledge base of 3D landslide. It can guide the research and prediction of unknown landslide. Finally, based on this research, the authors search out a most dangerous slip surface successfully by using Jiagou Sun landslide data in the loess, the results of this research have been verified.
Keywords :
artificial intelligence; climatology; data visualisation; disasters; earthquakes; geographic information systems; geology; geomorphology; knowledge based systems; 3D expression; 3D landslide; 3D loess slope geological disaster; 3D stability modulus; GIS-based 3D cell model; Jiagou Sun landslide data; artificial intelligence technology; climate; earthquake; forecasting 3D space; genetic algorithm; intelligent knowledge base construction; intelligent modeling; intelligent space-time prediction and analysis method; landslide disasters; landslide factors; landslide geological disasters; loess region; soil conditions; space-time model establishment; unfavorable slip surface; unknown landslide prediction; visual 3D information platform; Biological cells; Geographic Information Systems; Geology; Knowledge based systems; Solid modeling; Terrain factors; Three dimensional displays; GA; GIS; calculation of 3D stability; intelligent; knowledge base; landslide disaster; space-time prediction;
Conference_Titel :
Computer Science and Service System (CSSS), 2011 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-9762-1
DOI :
10.1109/CSSS.2011.5974655