Predicting harmful algae blooms

• Type: Article in International Scientific Journal
• Date: 2003

Authors

Ribeiro, R (Author) Other; Torgo, L (Author) FEP

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 7 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-000-KGZ

DOI: 10.1007/978-3-540-24580-3_36

Abstract (EN): In several applications the main interest resides in predicting rare and extreme values. This is the case of the prediction of harmful algae blooms. Though it's rare, the occurrence of these blooms has a strong impact in river life forms and water quality and turns out to be a serious ecological problem. In this paper, we describe a data mining method whose main goal is to predict accurately this kind of rare extreme values. We propose a new splitting criterion for regression trees that enables the induction of trees achieving these goals. We carry out an analysis of the results obtained with our method on this application domain and compare them to those obtained with standard regression trees. We conclude that this new method achieves better results in terms of the evaluation statistics that are relevant for this kind of applications.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5