Title
Modeling and simulation for natural disaster contingency planning driven by high-resolution remote sensing images.
Abstract
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 M&S systems still suffer from two open problems: (1) a lack of real data on natural disasters; 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.
Year
DOI
Venue
2014
10.1016/j.future.2013.12.018
Future Generation Computer Systems
Keywords
Field
DocType
Natural disasters,Contingency planning,Modeling and simulation,High-resolution remote sensing image,Dynamic data-driven application systems (DDDAS)
Computer science,Modeling and simulation,Remote sensing,Natural disaster,Information extraction,Crowd simulation,Landslide,Contingency plan
Journal
Volume
Issue
ISSN
37
C
0167-739X
Citations 
PageRank 
References 
11
0.64
23
Authors
8
Name
Order
Citations
PageRank
Minggang Dou1773.30
Jingying Chen28610.18
Dan Chen3109659.02
Xiaodao Chen4110.64
Ze Deng5583.16
Xuguang Zhang6110.64
Kai Xu7142.05
Jian Wang8110.64