Yiquan Song

Susceptibility assessment of earthquake-induced landslides using Bayesian network: A case study in Beichuan, China

Because of the uncertainties and complexities of the factors involved in causing landslides, it is generally difficult to analyze their influences quantitatively and to predict the probability of landslide occurrence. In this work, a hybrid method based on Bayesian network (BN) is proposed to analyze earthquake-induced landslide-causing factors and assess their effects. Our study area is Beichuan, China, where landslides have occurred in recent years, including mass landslides triggered by the 2008 Wenchuan earthquake. To provide a robust assessment of landslide probability, key techniques from landslide susceptibility assessment (LSA) modeling with BN are explored, including data acquisition and processing, BN modeling, and validation. In the study, eight landslide-causing factors were chosen as the independent variables for BN modeling. And this study shows that lithology and Arias intensity are the major factors affecting landslides in the study area. On the basis of the a posteriori probability distribution, the occurrence of a landslide is highly sensitive to relief amplitudes above 116.5m. Using a 10-fold cross-validation and a receiver operating characteristic (ROC) curve, the resulting accuracy of the BN model was determined to be 93%, which demonstrates that the model achieves a high probability of landslide detection and is a good alternative tool for landslide assessment.

Spatiotemporal simulation and risk analysis of dam-break flooding based on cellular automata

Chapinghe Barrier Lake was the largest among the barrier lakes formed in the aftermath of the magnitude 5.12 Wenchuan Earthquake. A rapid quantitative method for the evaluation of potential risk to lives and properties downstream was of the utmost importance for disaster management. The proposed method is based on spatiotemporal simulation using different dam-break scenarios and downstream hazard distribution analyses. This article adopts a cellular automata (CA) model to synthetically integrate multiple sets of geographic layers, including those containing the models needed for routine computation of flood hazards and those needed for vulnerability analysis of the people living downstream. A CA-based simulation and analysis method integrating hydrologic/hydraulic mechanisms is herein introduced, and relevant techniques are investigated. Our prototype experiment demonstrates that the proposed CA-based flood-hazard model can be conveniently integrated into a digital earth system and can further provide real-time simulation analyses of dam-break flood risks.

Real-time flood simulations using CA model driven by dynamic observation data

It is difficult to obtain accurate simulation results without observation data. So using real-time dynamic observation data in the simulation process has become an academic frontier of international research. This paper is a probing research on the data-driven adaptive modeling and automatic refactoring methods of flood routing simulation. A cellular automata (CA) data-driven flooding model was developed using the Hunhe River in Shenyang City as a case study. The proposed model can increase the accuracy of simulations by calculating differences in the water stages using high temporal resolution observational data. Meanwhile, corresponding parameter analysis was carried out based on the proposed CA model and the best lagging time between simulation and observation was discussed.

A grid-based spatial data model for the simulation and analysis of individual behaviours in micro-spatial environments

Abstract As crowd simulation in micro-spatial environment is more widely applied in urban planning and management, the construction of an appropriate spatial data model that supports such applications becomes essential. To address the requirements necessary to building a model of crowd simulation and people–place relationship analysis in micro-spatial environments, the concept of the grid as a basic unit of people–place data association is presented in this article. Subsequently, a grid-based spatial data model is developed for modelling spatial data using Geographic Information System (GIS). The application of the model for crowd simulations in indoor and outdoor spatial environments is described. There are four advantages of this model: first, both the geometrical characteristics of geographic entities and behaviour characteristics of individuals within micro-spatial environments are involved; second, the object-oriented model and spatial topological relationships are fused; third, the integrated expression of indoor and outdoor environments can be realised; and fourth, crowd simulation models, such as Multi-agent System (MAS) and Cellular Automata (CA), can be further fused for intelligent simulation and the analysis of individual behaviours. Lastly, this article presents an experimental implementation of the data model, individual behaviours are simulated and analysed to illustrate the potential of the proposed model.

Design and key techniques of a collaborative virtual flood experiment that integrates cellular automata and dynamic observations

With the development of computer science, communications technology and environmental modeling, virtual geographic environments (VGEs) have been linked with field observations and geographic modeling. VGEs enable researchers in various fields to collaboratively perform computer-aided geographic experiments. This study proposes a collaborative environment to conduct a virtual flood experiment that integrates cellular automata and dynamic observations. Some of the key techniques, including a cellular automata flood modeling method, a real-time parameter similarity evaluation method, and a collaborative visualization and operation method, are explored. The proposed techniques are tested with a prototype system as part of a flood simulation case study of the Hunhe River in Liaoning Province. We conclude that a virtual experiment environment can provide effective technical support for flood research.

Data integration and visualization: Dealing with massive and multi-dimensional marine spatial data

Marine observation has entered the era of three-dimensional observation at the present time. More and more spatial data is got form marine observing with no sophisticated visualization and analysis software for marine spatial data (MSD) either. Based on the analysis of data types and the acquisition methods in MSD, the paper proposed a data model for massive and multi-dimensional MSD. Then the procedure and implementation of MSD visualization is designed with the model. In addition, two applications for massive and multi-dimensional data visualization are presented.

Spatiotemporal analysis of land use/cover change patterns in the new coastal district of Tianjin, China

Land use/cover change is one of the most sensitive factors that show the interactions between human activities and the ecological environment. Since 1992, the government urged the development of the region of Bohai Gulf, the new coastal region of Tianjin has experienced great changes in land use/cover. Its urgent to detect the land use/cover change pattern to provide more explicit information on the further development of the new coastal district, which often requires to recover the history of land cover change and relates the spatio-temporal pattern of such change to other environmental and human factors, rather than merely relying on the change of areas or indices. This study makes Spatiotemporal analysis of trajectory land use/cover change patterns in the New Coastal District of Tianjin, after classifying the study area by Feature Extraction(FE) which first segments the whole area into objects and then classifies them based on the spectrum of the objects instead of pixels with Support Vector Machines (SVM) classifier. Multi-temporal and multi-source images of about one and a half decades were acquired for change detection, including Landsat TM, ETM+ and HJ1A images, and some land use maps were also used to supply training samples. First, all the images were geometrically corrected and registered on the same WGS84 coordinates. A unified land use classification plan was set up for all the images to classify land use type. The classification results were then utilized in the trajectory analysis of land use/cover change through the given three time nodes. Trajectories at every pixel were acquired to trace the history of land use/cover change for every location in the study area. Landscape metrics of change trajectories are also analyzed to detect the categorical change of different classes.

A framework for land-surface remote sensing data sharing and collaboration

In order to establish a collaborative decision making environment for scientists, we proposed an original approach to integrate remote sensing data and remote sensing analysis tools based on Web Service and GRID. The proposed framework consists of four tiers and provides three level services. Firstly, the analysis of RS information sharing is made with data resource sharing, computing resource sharing and knowledge resource sharing. Then, the framework of RS data sharing and collaboration is designed with the request of RS information sharing. The proposed framework consists of four tiers and can provide three level services. Finally, the implement of the framework is described. The three key systems, RS data retrieval and publishing system, RS processing service and Workflows system and Geo-collaboration and decision-making system are introduced in detail.