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Predicting Stream Nitrogen Concentration From Watershed Features Using Neural Networks

Lek, Sovan and Guiresse, Agnès Maritchù and Giraudel, J.-L. Predicting Stream Nitrogen Concentration From Watershed Features Using Neural Networks. (1999) Water Research, 3 (16). 3469-3478. ISSN 0043-1354

(Document in English)

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Official URL: http://dx.doi.org/10.1016/S0043-1354(99)00061-5


The present work describes the development and validation of an artificial neural network (ANN) for the purpose of estimating inorganic and total nitrogen concentrations. The ANN approach has been developed and tested using 927 nonpoint source watersheds studied for relationships between macro-drainage area characteristics and nutrient levels in streams. The ANN had eight independent input variables of watershed parameters (five on land use features, mean annual precipitation, animal unit density and mean stream flow) and two dependent output variables (total and inorganic nitrogen concentrations in the stream). The predictive quality of ANN models was judged with “hold-out” validation procedures. After ANN learning with the training set of data, we obtained a correlation coefficient r of about 0.85 in the testing set. Thus, ANNs are capable of learning the relationships between drainage area characteristics and nitrogen levels in streams, and show a high ability to predict from the new data set. On the basis of the sensitivity analyses we established the relationship between nitrogen concentration and the eight environmental variables.

Item Type:Article
Additional Information:Thanks to Elsevier editor. The definitive version is available at http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V73-3XH3VTW-B&_user=805612&_coverDate=11%2F30%2F1999&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1456902761&_rerunOrigin=google&_acct=C000043979&_version=1&_urlVersion=0&_userid=805612&md5=d3f9f8a0a454e4516c2506ffd07ff168&searchtype=a
HAL Id:hal-03607596
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Other partners > Université Montesquieu - Bordeaux 4 (FRANCE)
Laboratory name:
Deposited On:10 Sep 2010 13:49

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