Modeling and simulation for natural disaster contingency planning driven by high-resolution remote sensing images

Natural disasters occur unexpectedly and usually result in huge losses of life and property. How to effectively make contingency plans is an intriguing question constantly faced by governments and experts. Human rescue operations are the most critical issue in contingency planning. A natural disaster scenario is, in general, highly complicated and dynamic. Modeling and simulation technologies have been gaining considerable momentum in investigating natural disaster scenarios to enable contingency planning. However, existing MS and (2) the absence of methods and platforms to describe the collective behaviors of people in disaster situations. Considering these problems, an M&S framework for human rescue operations in a typical natural disaster, i.e., a landslide, has been developed in this study. The framework consists of three modules: (1) remote sensing information extraction, (2) landslide simulation, and (3)crowd simulation. The crowd simulation module is driven by the real/virtual data provided by the former modules. A number of simulations (using the Zhouqu landslide as an example) have been performed to study human relief operations spontaneously and under manipulation, with the effect of contingency plans highlighted. The experimental results demonstrate that (1) the simulation framework is an effective tool for contingency planning, and (2) real data can make the simulation outputs more meaningful. We enable evaluation of contingency plans using modelling and simulation technology.We model crowd behaviors under natural disasters.We develop a DDDAS simulation framework with the support of high resolution remote sensing information.

[1]  Aizhu Ren,et al.  Agent-based evacuation model of large public buildings under fire conditions , 2009 .

[2]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[3]  Lizhe Wang,et al.  Research Advances in Modern Cyberinfrastructure , 2010, New generation computing.

[4]  Yalchin Efendiev,et al.  A dynamic data-driven application simulation framework for contaminant transport problems , 2006, Comput. Math. Appl..

[5]  Lizhe Wang,et al.  Towards building a multi‐datacenter infrastructure for massive remote sensing image processing , 2013, Concurr. Comput. Pract. Exp..

[6]  Tony White,et al.  Macroscopic effects of microscopic forces between agents in crowd models , 2007 .

[7]  Albert Y. Zomaya,et al.  CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE Concurrency Computat.: Pract. Exper. (2012) Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/cpe.2839 SPECIAL ISSUE PAPER Energy efficient genetic-based schedulers in comp , 2022 .

[8]  Lizhe Wang,et al.  A general metric and parallel framework for adaptive image fusion in clusters , 2014, Concurr. Comput. Pract. Exp..

[9]  Karen Boyce,et al.  The Behaviour and Evacuation Experiences of WTC 9/11 Evacuees with self-designated mobility impairments , 2009 .

[10]  Juan Li,et al.  A comparative study of rate monotonic schedulability tests , 2012, The Journal of Supercomputing.

[11]  Dongkon Lee,et al.  A study on experiment of human behavior for evacuation simulation , 2004 .

[12]  Tao Yuan,et al.  Distributed data structure templates for data‐intensive remote sensing applications , 2013, Concurr. Comput. Pract. Exp..

[13]  Xiaolin Hu,et al.  Exploiting Sensor Spatial Correlation for Dynamic Data Driven Simulation of Wildfire , 2012, 2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation.

[14]  Nicolas Marmaras,et al.  Design of formative evacuation plans using agent-based simulation , 2007 .

[15]  Mehdi Mokhtarzade,et al.  Road detection from high-resolution satellite images using artificial neural networks , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[16]  Lizhe Wang,et al.  Performance evaluation of virtual machine-based Grid workflow system , 2008 .

[17]  Gregor von Laszewski,et al.  Towards building a cloud for scientific applications , 2011, Adv. Eng. Softw..

[18]  Shiyao Jin,et al.  A Multi-agent Simulation of Escape Panic , 2009, 2009 International Joint Conference on Computational Sciences and Optimization.

[19]  Lizhe Wang,et al.  Implementation and performance evaluation of the parallel CORBA application on computational grids , 2008, Adv. Eng. Softw..

[20]  Konrad Kulakowski,et al.  Agent-Based Approach in Evacuation Modeling , 2010, KES-AMSTA.

[21]  Richard E. Ewing,et al.  INTERPOLATION AND UPDATE IN DYNAMIC DATA-DRIVEN APPLICATION SIMULATIONS , 2007 .

[22]  Wentong Cai,et al.  A secure information service for monitoring large scale grids , 2007, Parallel Comput..

[23]  S. Sharma,et al.  Multi-agent modeling and simulation of human behavior in aircraft evacuations , 2008, IEEE Transactions on Aerospace and Electronic Systems.

[24]  Lizhe Wang,et al.  Review of performance metrics for green data centers: a taxonomy study , 2011, The Journal of Supercomputing.

[25]  Dinesh Manocha,et al.  Directing Crowd Simulations Using Navigation Fields , 2011, IEEE Transactions on Visualization and Computer Graphics.

[26]  Fusheng Yu,et al.  A New Validity Function for Fuzzy Clustering , 2009, 2009 International Conference on Computational Intelligence and Natural Computing.

[27]  Stan Z. Li,et al.  Markov Random Field Modeling in Image Analysis , 2001, Computer Science Workbench.

[28]  Lizhe Wang,et al.  Hybrid modelling and simulation of huge crowd over a hierarchical Grid architecture , 2013, Future Gener. Comput. Syst..

[29]  Kincho H. Law,et al.  Human and social behavior in computational modeling and analysis of egress , 2006 .

[30]  Xiaoyu Yang,et al.  Recent Research Advances in e-Science , 2009, Cluster Computing.

[31]  Xi He,et al.  Cloud Computing: a Perspective Study , 2010, New Generation Computing.

[32]  Samee Ullah Khan,et al.  Multi-level hierarchic genetic-based scheduling of independent jobs in dynamic heterogeneous grid environment , 2012, Inf. Sci..

[33]  Claire Johnson,et al.  Stay or Go? Human Behavior and Decision Making in Bushfires and Other Emergencies , 2012 .

[34]  Uday B. Desai,et al.  Senslide: a distributed landslide prediction system , 2007, OPSR.

[35]  Hai-Jun Huang,et al.  A potential field approach to the modeling of route choice in pedestrian evacuation , 2013 .

[36]  Gregor von Laszewski,et al.  Grid Virtualization Engine: Design, Implementation, and Evaluation , 2009, IEEE Systems Journal.

[37]  R. Hughes.  The flow of human crowds , 2003 .

[38]  Xiaolin Hu,et al.  A dynamic data driven application system for wildfire spread simulation , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

[39]  Geoffrey C. Fox,et al.  Task scheduling with ANN-based temperature prediction in a data center: a simulation-based study , 2011, Engineering with Computers.

[40]  Michael A. Regan,et al.  A comparison between actual and predicted evacuation times , 2001 .