![Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models [An article from: Environmental Modelling and Software]](https://www.ebooknetworking.net/books/B00/0RR/bigB000RR1IPW.jpg)
Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models [An article from: Environmental Modelling and Software]
Book Details
Author(s)F. Anctil, C. Perrin, V. Andreassian
PublisherElsevier
ISBN / ASINB000RR1IPW
ISBN-13978B000RR1IP9
MarketplaceFrance 🇫🇷
Description
This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Although attractive to hydrologists, artificial neural network modeling still lacks norms that would help modelers to create and train efficient rainfall-runoff models in a systematic way. This study focuses on the impact of the length of observed records on the performance of multiple-layer perceptrons (MLPs), and compare their results with those of a parsimonious conceptual model equipped with an updating scheme. Both models were assessed for 1-day-ahead stream flow predictions. Ninety-two different model scenarios were obtained for 1-, 3-, 5-, 9-, and 15-year time sub-series created from a 24-year training set, shifting by a 1-year sliding window. All the model scenarios were verified against the same 7-year test set. The results revealed that MLP stream flow mapping was efficient as long as wet weather data were available for the training; the longer series implicitly guarantee that the data contain valuable information of the hydrological behavior; the results were consistent with those reported for conceptual rainfall-runoff models. The physical knowledge in the conceptual models allowed them to make much better use of 1-year training sets than the MLPs. However, longer training sets were more beneficial to the MLPs than to the conceptual model. Both types shared best performance about evenly for 3- and 5-year training sets, but MLPs did better whenever the training set was dominated by wet weather. The MLPs continued to improve for input vectors of 9 years and more, which was not the case of the conceptual model.
Description:
Although attractive to hydrologists, artificial neural network modeling still lacks norms that would help modelers to create and train efficient rainfall-runoff models in a systematic way. This study focuses on the impact of the length of observed records on the performance of multiple-layer perceptrons (MLPs), and compare their results with those of a parsimonious conceptual model equipped with an updating scheme. Both models were assessed for 1-day-ahead stream flow predictions. Ninety-two different model scenarios were obtained for 1-, 3-, 5-, 9-, and 15-year time sub-series created from a 24-year training set, shifting by a 1-year sliding window. All the model scenarios were verified against the same 7-year test set. The results revealed that MLP stream flow mapping was efficient as long as wet weather data were available for the training; the longer series implicitly guarantee that the data contain valuable information of the hydrological behavior; the results were consistent with those reported for conceptual rainfall-runoff models. The physical knowledge in the conceptual models allowed them to make much better use of 1-year training sets than the MLPs. However, longer training sets were more beneficial to the MLPs than to the conceptual model. Both types shared best performance about evenly for 3- and 5-year training sets, but MLPs did better whenever the training set was dominated by wet weather. The MLPs continued to improve for input vectors of 9 years and more, which was not the case of the conceptual model.
