LiuPengXiangEtAl2012

Référence

Liu, Z., Peng, C., Xiang, W., Deng, X., Tian, D., Zhao, M., Yu, G. (2012) Simulations of runoff and evapotranspiration in Chinese fir plantation ecosystems using artificial neural networks. Ecological Modelling, 226:71-76. (Scopus )

Résumé

Runoff and evapotranspiration are two key variables of water budget in forest ecosystems. Modeling runoff and evapotranspiration dynamics play a vital role in assessing the hydrology cycle and function of forest ecosystems. Based on the hydrological and meteorological data collected over 20 years from January of 1988 to December of 2007 at Huitong National Forest Ecosystem Research Station, we used back propagation neural network (BPNN) and genetic neural network (GNN) models to simulate runoff and evapotranspiration of Chinese fir plantations for two watersheds located in Huitong county of Hunan Province, China. The purpose of this study was to accurately simulate runoff and evapotranspiration dynamics using both BPNN and GNN models. The model simulations of the runoff and evapotranspiration indicated that the GNN model concurrently possesses efficiency, effectiveness, and robustness. Moreover, the simulated results of GNN and BPNN model were compared with a multivariate statistics (M-slat) model. We found that the GNN model performed better than M-slat and BPNN models for modeling both runoff and evapotranspiration of Chinese fir plantations in China. © 2011 Elsevier B.V.

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@ARTICLE { LiuPengXiangEtAl2012,
    AUTHOR = { Liu, Z. and Peng, C. and Xiang, W. and Deng, X. and Tian, D. and Zhao, M. and Yu, G. },
    TITLE = { Simulations of runoff and evapotranspiration in Chinese fir plantation ecosystems using artificial neural networks },
    JOURNAL = { Ecological Modelling },
    YEAR = { 2012 },
    VOLUME = { 226 },
    PAGES = { 71-76 },
    ABSTRACT = { Runoff and evapotranspiration are two key variables of water budget in forest ecosystems. Modeling runoff and evapotranspiration dynamics play a vital role in assessing the hydrology cycle and function of forest ecosystems. Based on the hydrological and meteorological data collected over 20 years from January of 1988 to December of 2007 at Huitong National Forest Ecosystem Research Station, we used back propagation neural network (BPNN) and genetic neural network (GNN) models to simulate runoff and evapotranspiration of Chinese fir plantations for two watersheds located in Huitong county of Hunan Province, China. The purpose of this study was to accurately simulate runoff and evapotranspiration dynamics using both BPNN and GNN models. The model simulations of the runoff and evapotranspiration indicated that the GNN model concurrently possesses efficiency, effectiveness, and robustness. Moreover, the simulated results of GNN and BPNN model were compared with a multivariate statistics (M-slat) model. We found that the GNN model performed better than M-slat and BPNN models for modeling both runoff and evapotranspiration of Chinese fir plantations in China. © 2011 Elsevier B.V. },
    COMMENT = { Export Date: 16 May 2012 Source: Scopus CODEN: ECMOD doi: 10.1016/j.ecolmodel.2011.11.023 },
    ISSN = { 03043800 (ISSN) },
    KEYWORDS = { Back propagation neural network, Genetic neural network, Model validation, Nonlinear problem, Watersheds, Back propagation neural networks, Chinese fir, Forest ecosystem, Genetic neural network, Hunan province , China, Key variables, Meteorological data, Model simulation, Model validation, Multivariate statistics, National forests, Nonlinear problem, Simulated results, Water budget, Backpropagation, Dynamics, Ecosystems, Evapotranspiration, Forestry, Meteorology, Multivariant analysis, Neural networks, Runoff, Water supply, Watersheds, Computer simulation, artificial neural network, back propagation, coniferous forest, evapotranspiration, forest ecosystem, hydrometeorology, model validation, multivariate analysis, nonlinearity, plantation, runoff, spatiotemporal analysis, water budget, watershed, Ecosystems, Forestry, Meteorology, Neural Networks, Problem Solving, Runoff, Simulation, Water Sheds, Water Supply, China, Huitong, Hunan, Cunninghamia lanceolata },
    OWNER = { Luc },
    TIMESTAMP = { 2012.05.16 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-84455200563&partnerID=40&md5=8d982be98820497f51a72b13ad380eac },
}

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