Natsuko Sugaya

Spatio-temporal similarity search method for disaster estimation

For fast disaster estimation after a large-scale disaster occurs, this paper presents a fast spatio-temporal similarity search method that searches a database storing many scenarios of disaster simulation results represented by time-series grid data for some scenarios similar to insufficient observed data sent from sensors. The proposed method efficiently processes spatio-temporal intersection by using a spatiotemporal index to reduce the processing time for the spatiotemporal similarity search. Additionally, this paper presents the efficient spatio-temporal range search method by using this spatio-temporal index. The spatio-temporal range search is needed for the analysis and visualization in order to grasp a damage situation after spatio-temporal similarity search returns some scenarios similar to observed data. The results of the performance evaluation show that the proposed method has a shorter response time for the spatiotemporal similarity search than two conventional methods that use a temporal index and a spatial index. They also show that the response time is within about 30 seconds when the proposed method searches the database storing 50 billion time-series grid data items for some scenarios similar to 100 observed data items. As a result, the proposed method can be applied to a real environment in which a spatio-temporal similarity search needs to processed within 10 minutes. Additionally, the evaluation results show that the spatio-temporal range search method by using the spatio-temporal index can be also applied to a real environment.

Composition of simulation data for large-scale disaster estimation

When a large-scale natural disaster occurs, it is necessary to quickly collect damage information so that disaster-relief operations and wide-area support in accordance with the scale of the natural disaster can be initiated. Previously, we proposed a fast spatio-temporal similarity search method (called the STSim method) that searches a database storing many scenarios of disaster simulation data represented by time-series grid data for scenarios similar to insufficient observed data sent from sensors. When the STSim method is naively applied for estimating disasters occurring at multiple locations, e.g., fire spreading after a large-scale earthquake, it must prepare a huge number of combinations consisting of scenarios that represent disasters at multiple locations. This paper presents a combination method of simulation data in order to apply the STSim method for estimating disasters occurring at multiple locations. This proposed method stores scenarios, each of which represents a disaster occurring at a single location, to a database; thus, reducing the number of scenarios. After a disaster occurs, it extracts and composes scenarios similar to observed data, resulting in efficient disaster estimation in any situation. We conducted performance evaluations under the assumption that an earthquake occurs below the Tokyo metropolitan region and estimating the spread of fire in the initial response. These results of the processing time for estimating the spread of fire show that the processing time is within 10 minutes, which is practical.