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A platform for research: civil engineering, architecture and urbanism
Real‐Time Flood Forecasting Using Neural Networks
Real‐time forecasting of stream flows during storms provides an essential input to operational flood management. This work is usually very complex owing to the uncertain and unpredictable nature of the underlying phenomena. The technique of neural networks therefore was applied to model it. Forecasting of flood values during storms with a lead time of one and more hours was made using a selected sequence of past flood values observed at a specific location. Training of the network was done with the help of three alternative methods, viz., error backpropagation, conjugate gradient, and cascade correlation. Resulting flood forecasts were found to be satisfactory—especially when warning time was the least.
Real‐Time Flood Forecasting Using Neural Networks
Real‐time forecasting of stream flows during storms provides an essential input to operational flood management. This work is usually very complex owing to the uncertain and unpredictable nature of the underlying phenomena. The technique of neural networks therefore was applied to model it. Forecasting of flood values during storms with a lead time of one and more hours was made using a selected sequence of past flood values observed at a specific location. Training of the network was done with the help of three alternative methods, viz., error backpropagation, conjugate gradient, and cascade correlation. Resulting flood forecasts were found to be satisfactory—especially when warning time was the least.
Real‐Time Flood Forecasting Using Neural Networks
Thirumalaiah, Konda (author) / Deo, M. C. (author)
Computer‐Aided Civil and Infrastructure Engineering ; 13 ; 101-111
1998-11-01
11 pages
Article (Journal)
Electronic Resource
English
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