Using A Neural Network To Build A Hydrologic
Model Of The Big Thompson River
Author(s)
W. S. Huffman & A. K. Mazouz
Abstract
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out of date almost as soon as the surveyors have put away their transits.
Keywords
flood forecasting, neural networks, hydrologic modelling, rainfall/
runoff, hydrology, modelling, artificial neural networks.
Related Book
Other papers in this volume
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