Finding Relevant Sequences With The Least Temporal Contradiction Measure: Application to Hydrological Data

In this paper, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we apply an algorithm to extract sequential patterns on data collected at stations located along several rivers. The data is pre-processed in order to obtain different spatial proximities and the number of patterns is estimated to highlight the influence of defined spatial relationship. We provide an objective measure of assessment, called the least temporal contradiction, to help the expert in discovering new knowledge. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and rivers monitoring pressure data.