Research Repository

Exceptional Object Analysis for Finding Rare Environmental Events from water quality datasets

He, Jing and Zhang, Yanchun and Huang, Guangyan (2012) Exceptional Object Analysis for Finding Rare Environmental Events from water quality datasets. Neurocomputing, 92 (1). pp. 69-77. ISSN 0925-2312

Full text for this resource is not available from the Research Repository.

Abstract

The earth's environment changes with time, as a result of the forces of nature. However, it is the activity of humans that negatively impacts the environment and causes unusual environmental events. Excluding the human factor, the environment will eventually and predictably change, this change being generally global. We assume that the earth's environment will be resilient for hundreds and thousands of years, thus normal environmental events that exhibit common and predictable trends should be the norm. However, exceptional events brought about by humans should be the minority; otherwise, the environment will soon be totally destroyed. Our goal in this paper is to find rare environmental events (e.g. water pollution events) from water quality datasets.

Item Type: Article
Additional Information:

This work was partially supported by an Australian Research Council (ARC) Linkage Project (Real-time and Self- Adaptive Stream Data Analyser for Intensive Care Management)

Uncontrolled Keywords: ResPubID25438, exceptional object analysis, EOA, clustering, anomaly detection, rare environmental event, support vector machine, SVM
Subjects: FOR Classification > 0502 Environmental Science and Management
FOR Classification > 0801 Artificial Intelligence and Image Processing
Faculty/School/Research Centre/Department > College of Science and Engineering
Funders: http://purl.org/au-research/grants/arc/LP100200682
Depositing User: Yimin Zeng
Date Deposited: 10 Nov 2013 03:41
Last Modified: 21 Aug 2014 03:57
URI: http://vuir.vu.edu.au/id/eprint/22123
DOI: 10.1016/j.neucom.2011.08.036
ePrint Statistics: View download statistics for this item
Citations in Scopus: 0 - View on Scopus

Repository staff only

View Item View Item

Search Google Scholar