000			02186nam a22002897a 4500
003			ZW-GwMSU
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008			230530b |||||||| |||| 00| 0 eng d
040			_aMSU _cMSU _erda
100			_aWANG, Fei
245			_aChlorophyll a simulation in a Lake ecosystem using a model with wavelet analysis and artificial neural network
264			_aNew York _bSpringer _c2013
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_a Environmental Management _vVolume , number ,
520			_aAccurate and reliable forecasting is important for the sustainable management of ecosystems. Chlorophyll a (Chl a) simulation and forecasting can provide early warning information and enable managers to make appropriate decisions for protecting lake ecosystems. In this study, we proposed a method for Chl a simulation in a lake that coupled the wavelet analysis and the artificial neural networks (WA–ANN). The proposed method had the advantage of data preprocessing, which reduced noise and managed nonstationary data. Fourteen variables were included in the developed and validated model, relating to hydrologic, ecological and meteorologic time series data from January 2000 to December 2009 at the Lake Baiyangdian study area, North China. The performance of the proposed WA–ANN model for monthly Chl a simulation in the lake ecosystem was compared with a multiple stepwise linear regression (MSLR) model, an autoregressive integrated moving average (ARIMA) model and a regular ANN model. The results showed that the WA-ANN model was suitable for Chl a simulation providing a more accurate performance than the MSLR, ARIMA, and ANN models. We recommend that the proposed method be widely applied to further facilitate the development and implementation of lake ecosystem management.
650			_achlorophyll a simulation
650			_awavelet transformation
650			_aartificial neural networks
700			_aWANG, Xuan
700			_aCHEN, Bin
700			_aZHAO, Ying
700			_aYANG, Zhifeng
856			_uhttps://doi.org/10.1007/s00267-013-0029-5
942			_2lcc _cJA
999			_c162444 _d162444