Derivation of a fuzzy national phosphorus export model using 84 Irish catchments

Implementation of appropriate management strategies to mitigate diffuse phosphorus (P) pollution at the catchment scale is vitally important for the sustainable development of water resources in Ireland. An important element in the process of implementing such strategies is the prediction of their impacts on P concentrations in a catchment using a reliable mathematical model. In this study, a state-of-the-art adaptive neuro-fuzzy inference system (ANFIS) has been used to develop a new national P model capable of estimating average annual ortho-P concentrations at un-gauged catchments. Data from 84 catchments dominated by diffuse P pollution were used in developing and testing the model. Six different split-sample scenarios were used to partition the total number of the catchments into two sets, one to calibrate and the other to validate the model. The k-means clustering algorithm was used to partition the sets into clusters of catchments with similar features. Then for each scenario and for each cluster case, 11 different models, each of which consists of a linear regression sub-model for each cluster, were formulated by using different input variables selected from among six spatially distributed variables including phosphorus desorption index (PDI), runoff risk index (RRI), geology (GEO), groundwater (GW), land use (LU), and soil (SO). The success of the new approach over the conventional lumped, empirical, modelling approach was evident from the improved results obtained for most of the cases. In addition the results highlighted the importance of using information on PDI and RRI as explanatory input variables to simulate the average annual ortho-P concentrations.