Using A Neural Network To Build A Hydrologic Model Of The Big Thompson River
Free (open access)
W. S. Huffman & A. K. Mazouz
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 Neural Networks can be successfully applied to hydrologic modeling. The river system chosen for the research was the Big Thompson River, located in North-central Colorado, United States of America. The Big Thompson River is a snow melt controlled river that runs through a steep, narrow canyon. In 1976, the canyon was the site of a devastating flood that killed 145 people and resulted in millions of dollars of damage. 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 by Dr. A. Kader Mazouz on the same data. The Neural Network’s predictions exceeded those of the multiple regression analysis by significant margins in all measurement criteria. Keywords: flood forecasting, neural networks, hydrologic modelling, rainfall/ runoff, hydrology, modelling, artificial neural networks. 1 Introduction One of the major problems in flood disaster response is that floodplain data are out of date almost as soon as the surveyors have put away their transits.
flood forecasting, neural networks, hydrologic modelling, rainfall/ runoff, hydrology, modelling, artificial neural networks.