Hydrologic Modeling of the Big Thompson River Using a Neural Network

Methods of modeling the hydrologic process range from human observers to sophisticated surveys and statistical analysis of climatic data. In the last few years, researchers have applied computer programs called neural networks or artificial neural networks to a variety of uses ranging from medical to financial. The purpose of the study was to demonstrate that commercial neural networks can be successfully applied to hydrologic modeling. Using publicly available climatic and stream flow data and a ward systems neural network, the study resulted in prediction accuracy of greater than 97% in +/-100 cubic feet per minute range. The average error of the predictions was less than 16 cubic feet per minute. To further validate the model´s predictive capability, a multiple regression analysis was done on the same data. The neural network´s predictions exceeded those of the multiple regression analysis by significant margins in all measurement criteria.