Assessment of hydrological and seasonal controls over the nitrate flushing from a forested watershed using a data mining technique

A data mining, regression tree algorithm M5 was used to review the understanding of mutual hydrological and seasonal settings which control the streamwater nitrate flushing during hydrological events within a forested watershed in the southwestern part of Slovenia, characterized by distinctive flushing, almost torrential hydrological regime. 5 The basis for the research presented an extensive dataset of continuous, high frequency measurements of seasonal meteorological conditions, watershed hydrological responses and streamwater nitrate concentrations. The dataset contained 16 recorded hydrographs occurring in different seasonal and hydrological conditions. Based on predefined regression tree pruning criteria, a comprehensible regression tree model was 10 obtained in the sense of the domain knowledge, which was able to adequately describe most of the streamwater nitrate concentration variations (RMSE=1.02mg/l-N; r=0.91). The attributes which were found to be the most descriptive in the sense of streamwater nitrate concentrations were the antecedent precipitation index (API) and air temperatures in the preceding periods. The model was most successful in describ15 ing streamwater concentrations in the range 1–4mg/l-N, covering large proportion of the dataset. The model performance was poorer during the periods of high streamwater nitrate concentration oscillations (up to 7mg/l-N during the summer hydrographs and 14mg/l-N during the extreme November hydrograph) related to highly variable hydrological conditions, which would require a less robust regression tree model. 20