• 论文 •

### Comprehensive Analysis and Artificial Intelligent Simulation of Land Subsidence of Beijing, China

ZHU Lin1, 2, GONG Huili1, 2, LI Xiaojuan1, 2, LI Yongyong1, 2, SU Xiaosi3, GUO Gaoxuan4

1. (1. College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China; 2. Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing 100048, China; 3. College of Environment and Resources, Jilin University, Changchun 130012, China; 4. China Hydrogeology and Engineering Geology Team of Beijing, Beijing 100958, China)
• 出版日期:2013-03-25 发布日期:2013-03-25

### Comprehensive Analysis and Artificial Intelligent Simulation of Land Subsidence of Beijing, China

ZHU Lin1, 2, GONG Huili1, 2, LI Xiaojuan1, 2, LI Yongyong1, 2, SU Xiaosi3, GUO Gaoxuan4

1. (1. College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China; 2. Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing 100048, China; 3. College of Environment and Resources, Jilin University, Changchun 130012, China; 4. China Hydrogeology and Engineering Geology Team of Beijing, Beijing 100958, China)
• Online:2013-03-25 Published:2013-03-25

Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy.

Abstract:

Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy.