Research on Water Supply Reservoir Operating Rules Extraction Based on Artificial immune Recognition System

DOI

10.2991/iske.2007.24How to use a DOI?

Keywords

water supply; operating rules; information entropy; data mining; artificial immune recognition system

Abstract

In this paper artificial immune recognition system (AIRS) is employed as an emerging technique of data mining to extract the reservoir operating rules with a case of water supply reservoir, and we mainly focus on the impacts of learning mechanisms of AIRS on the obtained operation rules, therefore the mechanisms are explored and different gene encodings, as knowledge representatives, and the uncertainties of annual hydrological conditions (AHC), one attribute of the operating data, are considered. In order to further illuminate the learning capabilities, the classification results of the rules through AIRS and RBF networks are compared, indicating AIRS can be better for mining the reservoir operating rules which are of more transparent and interpretive, and can be dynamically updated.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xianfeng Zhang
AU  - Xiaolin Wang
AU  - Zhengjie Yin
AU  - Huiqiang Li
PY  - 2007/10
DA  - 2007/10
TI  - Research on Water Supply Reservoir Operating Rules Extraction Based on Artificial immune Recognition System
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 136
EP  - 143
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.24
DO  - 10.2991/iske.2007.24
ID  - Zhang2007/10
ER  -