Neural network integration of field observations for soil endocrine disruptor characterisation

A neural network approach was used to predict the presence and concentration of a range of endocrine disrupting compounds (EDCs), based on field observations. Soil sample concentrations of endocrine disrupting compounds (EDCs) and site environmental characteristics, drawn from the National Soil Inventory of Scotland (NSIS) database, were used. Neural network models were trained to predict soil EDC concentrations using field observations for 184 sites. The results showed that presence/absence and concentration of several of the EDCs, mostly no longer in production, could be predicted with some accuracy. We were able to predict concentrations of seven of 31 compounds with r2 values greater than 0.25 for log-normalised values and of eight with log-normalised predictions converted to a linear scale. Additional statistical analyses were carried out, including Root Mean Square Error (RMSE), Mean Error (ME), Willmott's index of agreement, Percent Bias (PBIAS) and ratio of root mean square to standard deviation (RSR). These analyses allowed us to demonstrate that the neural network models were making meaningful predictions of EDC concentration. We identified the main predictive input parameters in each case, based on a sensitivity analysis of the trained neural network model. We also demonstrated the capacity of the method for predicting the presence and level of EDC concentration in the field, identified further developments required to make this process as rapid and operator-friendly as possible and discussed the potential value of a system for field surveys of soil composition.