DEVELOPMENT OF AN ARTIFICIAL NEURAL NETWORK FOR HYDROLOGIC AND WATER QUALITY MODELING OF AGRICULTURAL WATERSHEDS

Abstract

Agriculture is the leading source of nonpoint-source pollution on a national scale. The driving force ofnonpoint -source pollution is the rainfall-runoff process, which is the transformation of rainfall to streamflow. This is acomplex, nonlinear, time-varying, and spatially distributed process on the watershed scale that is difficult to effectively modelby conventional, deterministic means. Artificial neural networks (ANNs) offer a new approach to forecasting the hydrologicand water quality response of a watershed system. The goal of this work is to develop an ANN model as a long-term forecastingtool for predicting the hydrology and water quality of agricultural watersheds where the physical processes are difficult tomodel using traditional hydrologic/water quality models. The chosen form of neural network is a flexible mathematicalstructure, which is capable of identifying complex nonlinear relationships between input and output data sets. In this article,a multi-layer, feedforward ANN model was developed and tested using historical daily rainfall, streamflow, and nitrate datafrom the Vermilion River in Illinois, a watershed with intensive subsurface drainage and historically high nitrateconcentrations. The ANN was applied to predict daily streamflow and nitrate load based on rainfall. The results show highlyaccurate performance of the ANN model (r2 values > 0.80) in predicting daily streamflow and nitrate loads.