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

Abstract

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.

Keywords:

• virtual geographic experiment
• dynamic data-driven
• flood simulation
• cellular automata

Acknowledgments

This research was supported by the National Natural Science Foundation of China (41101363), the Key Knowledge Innovative Project of the Chinese Academy of Sciences (KZCX2-EW-318), the National Natural Science Foundation of China (41471341, 41201375), ‘135’ Strategy Planning (grant no. Y3SG1500CX) of the Institute of Remote Sensing and Digital Earth, CAS, Tianjin Research Program of Application Foundation and Advanced Technology (14JCQNJC07900).