A Knowledge Discovery Process for Spatiotemporal Data: Application to River Water Quality Monitoring

TitreA Knowledge Discovery Process for Spatiotemporal Data: Application to River Water Quality Monitoring
Publication TypeJournal Article
Year of Publication2015
AuthorsAlatrista-Salas, H, Azé, J, Bringay, S, Cernesson, F, Selmaoui-Folcher, N, Teisseire, M
JournalEcological Informatics
Volume26
Issue2
Pagination127-139
Date Published03/2015
Type of ArticleJournal
Abstract

Rapid population growth, and human activities (such as agriculture, industry, transports,...) development have increased vulnerability risk for water resources. Due to the complexity of natural processes and the numerous
interactions between hydro-systems and human pressures, water quality is dicult to be quanti ed. In this context, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we combine successive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre-processed in order to obtain di erent
spatial proximities. Later, we apply a standard algorithm to extract sequential patterns. Finally we propose a combination of two techniques (1) to flter patterns based on interest measure, and; (2) to group and present them
graphically, to help the experts. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and river monitoring pressure data.

URLhttp://ac.els-cdn.com/S1574954114000570/1-s2.0-S1574954114000570-main.pdf?_tid=d4d2bab2-3c96-11e4-94d2-00000aab0f01&acdnat=1410758104_4b53fe57581bf0734fcbf915d8b07197
DOI10.1016/j.ecoinf.2014.05.011