Using A Neural Network Model To Forecast Flood
Events On The Big Thompson River
Author(s)
W. S. Huffman & A. K. Mazouz
Abstract
For the entire period of recorded time, floods have been a major cause of loss of
life and property. Methods of prediction and mitigation 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 flood forecasting.
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 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.
Keywords
flood forecasting, neural networks, hydrologic modelling, rainfall/runoff, hydrology, modelling, artificial neural networks.
Related Book
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