A new approach of obtaining reservoir operation rules: Artificial immune recognition system

  • Authors:

  • Xiao-Lin Wang;Jin-Hua Cheng;Zheng-Jie Yin;Ming-Jing Guo

  • Affiliations:

  • School of Economic and Management, China University of Geosciences, Wuhan 430074, China;School of Economic and Management, China University of Geosciences, Wuhan 430074, China;Water Resource Department, Yangtze River Scientific Research Institute, Wuhan 430010, China;School of Economic and Management, China University of Geosciences, Wuhan 430074, China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

Artificial immune recognition system (AIRS) was employed in this paper as a new approach of data mining to extract operating rules on a case of water-supply reservoir, and the comparisons were performed between the operating rules obtained by the system and those by RBF. Further statistics about distance distributions between the acquired operating rules and training or testing samples are made to indirectly illuminate the impacts on the performances or behaviors of AIRS from three aspects of different affinity functions, training (testing) sample spatial distribution and supplementary samples in high nonlinear space of operation decision. The results indicate that AIRS can effectively extract water-supply operating rules and enrich the reservoir operation researches.