Emergency Response Decision Support System

von: Siqing Shan, Qi Yan

Springer-Verlag, 2017

ISBN: 9789811035425 , 70 Seiten

Format: PDF

Kopierschutz: Wasserzeichen

Windows PC,Mac OSX geeignet für alle DRM-fähigen eReader Apple iPad, Android Tablet PC's

Preis: 53,49 EUR

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Emergency Response Decision Support System


 

This book mainly addresses the Emergency Response Decision Support System (ERDSS) and its applications, making use of ten related modules and a number of key technologies, especially Disaster Assessing Technology, Adaptive Information Evaluation Technology and Knowledge Management Technology. The book is especially valuable in coping with disasters that result in the loss of human life and property, and which threaten the stability of our societies. The ERDSS enables people to prepare for potential incidents, to rapidly respond to them, and to cope with their aftermath. Presenting practical solutions, this book helps readers to understand the ERDSS and effectively respond to emergency events.

Siqing Shan is a professor from Beihang University and deputy director of Beijing Key Laboratory of Urban Emergency Support Simulation Technology. His research interests include emergency management, decision support, information systems, and big data analytics. He has published more than 50 research papers in journals such as IJPE, EIS, ITM, CIE, SRBS and for international conferences such as the ICIM, CIE, CEBM and so on. He received a five best paper award from the ICIM2012. He is responsible for two National Natural Science Fund of China (No. 71471008 and No. 70971004) projects, and has completed two emergency response projects funded by Beijing municipal government. In these projects, he studied the framework, key technologies, and application of an emergency response decision support system; this book is primarily based on the findings. 

Qi Yan is a doctoral candidate in Management Science and Engineering at Beihang University. She is mainly engaged in research on emergency management, urban disaster loss assessment, and UGC (User Generated Content) feature analysis.