@article {48640,
	title = {Effect of watershed subdivision on water-phase phosphorus modelling : an artificial neural network modelling application},
	volume = {7},
	year = {2008},
	pages = {S95-S108},
	publisher = {Journal of  Environmental Engineering and Science},
	abstract = { This study is an effort to incorporate low-cost time-variant remote sensing (RS) information in watershed-scale total phosphorus (TP) modelling. Four watershed subdivisions were delineated to assess the impact of watershed subdivision on the prediction accuracy of TP concentration in stream water. Four TP artificial neural network (ANN) models were designed to incorporate RS data into a semi-distributed approach. The remotely derived enhanced vegetation index and the normalized difference water index were successful in representing vegetation dynamics in the devised models. The models were applied to a 15.6 km2 watershed in the Canadian Boreal Plain. Eight measures of goodness-of-fit statistics were used for model evaluation. Although statistical model evaluation did favour the finest resolution in this case study, the differences in performance indicators among the four models were insignificant for any practical application. The encouraging results from this exercise demonstrate the applicability of the ANN semi-distributed modelling approach and the usefulness of RS data in simulating TP dynamics. Such models can potentially serve as valuable tools for watershed-scale forest management. Cette étude représente un effort pour incorporer de l{\textquoteright}information de télédétection variable dans le temps et à faible coût dans la modélisation du phosphore total à l{\textquoteright}échelle du bassin versant. Quatre subdivisions du bassin versant ont été délimitées afin d{\textquoteright}évaluer l{\textquoteright}impact de la subdivision sur la précision de la prévision de la concentration en phosphore total dans l{\textquoteright}eau de ruisseau. Quatre modèles de réseaux de neurones artificiels du phosphore total ont été conçus afin d{\textquoteright}incorporer les données de télédétection dans une approche semi-distribuée. L{\textquoteright}indice de végétation avancé dérivé et l{\textquoteright}indice normalisé de la variation d{\textquoteright}eau représentent adéquatement la dynamique de la végétation dans les modèles élaborés. Les modèles ont été appliqués à un bassin versant de 15,6 km2 dans la plaine boréale canadienne. Huit mesures de statistiques de la qualité de l{\textquoteright}ajustement ont été utilisées pour évaluer le modèle. Bien que l{\textquoteright}évaluation du modèle statistique ait favorisé la meilleure résolution dans cette étude de cas, les différences dans les indicateurs de rendement des quatre modèles n{\textquoteright}étaient, à toutes fins pratiques, pas importantes. Les résultats encourageants de cet exercice montrent l{\textquoteright}applicabilité de l{\textquoteright}approche de modélisation semi-distribuée aux réseaux de neurones artificiels ainsi que l{\textquoteright}utilité des données obtenues par télédétection dans la simulation de la dynamique du phosphore total. De tels modèles pourraient servir d{\textquoteright}outils valables pour la gestion des forets à l{\textquoteright}échelle du bassin versant.  Read less },
	url = {http://article.pubs.nrc-cnrc.gc.ca/RPAS/rpv?hm=HInit\&calyLang=eng\&journal=jees\&volume=7\&afpf=s08-043.pdf},
	author = {Nour, Mohamed H. and Smith, Daniel W. and El-Din, Mohamed Gamal and Prepas, Ellie E.}
}
