Abstract
Nutrient runoff is an environmental concern. There is a need for a robust method of predicting nutrient concentrations in runoff
from feedlots to facilitate management practices. To investigate the feasibility of using multiple linear regression (MLR) and
artificial neural network (ANN) in predicting nutrients in runoff, simple water quality paramters (e.g., pH and EC) of runoff from
a feedlot in North Dakota, USA, were used to train an ANN. Both models accurately predicted potassium concentration based on
inputs pH and electrical conductivity (EC). The ANN approach, however, used in this study gave a better prediction than
multiple linear regression models. It may be concluded that ANN may be a useful tool for predicting nutrients concentration in
runoff using pH and EC, when expensive and time-consuming analytical data are not available, and this information may be used
for implementing measures to minimize environmental concerns.