Machine Learning
Computers and Electronics in Agriculture
| Hi-index | 0.02 |
A wide variety of watershed-scale attributes can be used as predictors of the export of dissolved organic carbon (DOC) from a watershed. However, the complexity and number of relationships makes the development of generally applicable mechanistic models for prediction of DOC export based on measurement of factors difficult. Here we have applied neural network modelling methods to the prediction of stream flux and daily DOC export from several watersheds of varying size within the Dee valley, in north-east Scotland. A two-stage process was carried out in which first a model was developed which used a large number of variables thought to be relevant to DOC export, and then the possibility of using a restricted set of variables was investigated in order to reduce the amount of analysis required in order to produce accurate DOC export predictions. The results showed that it is possible to predict DOC export using input variables corresponding broadly to the factors responsible for soil formation, and that a single sample site may provide enough information to allow prediction for an entire watershed. However, in order to achieve a model with statistically significant results, it is necessary to use multiple sample sites per watershed, and to use measured rather than modelled flow values. Discussion is made of the effectiveness of the neural network method in developing models of DOC export, and of problems with the method (particularly in the inability to use NN models for process-based models).