title: Integration of multi-technology on oil spill emergency preparedness.
abstract: This paper focuses on the integration of technologies including Case-Based Reasoning (CBR), Genetic Algorithm (GA) and Artificial Neural Network (ANN) for establishing emergency preparedness for oil spill accidents. In CBR, the Frame method is used to define case representation, and the HEOM (Heterogeneous Euclidean-Overlap Metric) is improved to define the similarity of case properties. In GA, we introduce an Improved Genetic Algorithm (IGA) that achieves case adaptation, in which technologies include the Multi-Parameter Cascade Code method, the Small Section method for generation of an initial population, the Multi-Factor Integrated Fitness Function, and Niche technology for genetic operations including selection, crossover, and mutation. In ANN, a modified back-propagation algorithm is employed to train the algorithm to quickly improve system preparedness. Through the analysis of 32 fabricated oil spill cases, an oil spill emergency preparedness system based on the integration of CBR, GA and ANN is introduced. In particular, the development of ANN is presented and analyzed. The paper also discusses the efficacy of our integration approach.
authors: Key Laboratory of Yangtze River Water Environment of Ministry of Education, Tongji University, Shanghai, China. zl_liao@tongji.edu.cn
authors: Zhenliang
authors: Z
authors: Liao
authors: Phillip M
authors: PM
authors: Hannam
authors: Xiaowei
authors: X
authors: Xia
authors: Tingting
authors: T
authors: Zhao
mesh_terms: D000465:Algorithms; D002959:Civil Defense / Q000379:methods*; D052918:Environmental Restoration and Remediation; D016571:Neural Networks, Computer; D059392:Petroleum Pollution / Q000032:analysis* / Q000517:prevention & control / Q000706:statistics & numerical data