Researches of Water Bloom Emergency Management Decision-making Method and System Based on Fuzzy Multiple Attribute Decision Making
The prevention and management of water blooms is an important measure of environment protection. At present, although there is a variety of research achievement in water bloom management methods, the related reports in water blooms emergency management decision are hardly ever. Because the forming mechanism of water bloom is still unknown, it is difficult to come up with optimal water bloom management decision-making methods. Based on the deep research of mechanism characteristic and emergency management decision model of water bloom, this paper puts forward a Multiple Attribute Decision Making (MADM) based on water bloom emergency management decision-making methods, and applying to the lake reservoir water bloom emergency management programs selection, making model validation according to the lake reservoir as well, and then providing effective informational decision basis for environmental protection department to prevent and manage the water bloom.
Keywords: formation mechanism of water bloom, emergency management decision, Multiple Attribute Decision Making methods
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ABOUT THE AUTHORS
Zaiwen Liu
Dr. Zaiwen LIU received the Ph.D. degree in automatic control science & engineering from Beijing Institute of Technology, in 2006. Currently, he is an Professor at Beijing Technology and Business University. His interests are in intelligent detect and optimal control system, water bloom predicting and optimal decision.
Lin Li
Department of Automation, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048,China
Xiaoyi Wang
Chen Chen
Zaiwen Liu
Dr. Zaiwen LIU received the Ph.D. degree in automatic control science & engineering from Beijing Institute of Technology, in 2006. Currently, he is an Professor at Beijing Technology and Business University. His interests are in intelligent detect and optimal control system, water bloom predicting and optimal decision.
Lin Li
Department of Automation, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048,China
Xiaoyi Wang
Chen Chen