Discriminant temporal patterns for linking physico-chemistry and biology in hydro-ecosystem assessment

We propose a new data mining process to extract original knowledge from hydro-ecological data, in order to help the identification of pollution sources. This approach is based (1) on a domain knowledge discretization (quality classes) of physico-chemical and biological parameters, and (2) on an extraction of temporal patterns used as discriminant features to link physico-chemistry with biology in river sampling sites. For each bio-index quality value, we obtained a set of significant discriminant features. We used them to identify the physico-chemical characteristics that impact on different biological dimensions according to their presence in extracted knowledge. The experiments meet with the domain knowledge and also highlight significant mismatches between physico-chemical and biological quality classes. Then, we discuss about the interest of using discriminant temporal patterns for the exploration and the analysis of temporal environmental data such as hydro-ecological databases. •We use temporal pattern mining to link physico-chemical and biological dimensions in hydrobiological data.•Our process uses the domain knowledge to discretize the data in quality classes.•We only filter fifteen discriminant patterns for different biological quality classes.•Extracted knowledge provides new knowledge and perspectives for hydro-ecosystem assessment.