Estimation of Chlorophyll-a Concentration in Taihu Lake by Using Back Propagation (BP) Neural Network Forecast Model

doi:10.3969/j.issn.1000-6362.2016.04.004

Abstract

Abstract:

To estimate chlorophyll-a concentration in the centre area of Taihu Lake, back propagation (BP) neural network forecast model was constructed based on principal component analysis according to conventional water quality monitoring data and meteorological data in Taihu from 2001 to 2006 and the sensitivity analysis of model was performed. The results showed that in the centre area of Taihu Lake, estimated value of chlorophyll-a concentration according to BP neural network forecast model had a better fit with the measured data of chlorophyll-a concentration. Through sensitivity analysis of established estimation model, it was found that temperature and dissolved oxygen were highly related with the chlorophyll-a concentrations. At the same time, chlorophyll-a concentrations in different areas of Taihu Lake (Meiliang Bay, Gonghu Bay, Zhushan Bay and East Taihu) were estimated by using BP neural network forecast model, close agreement was observed between estimated and the measured data of chlorophyll-a concentration. In general, BP neural network forecast model could be used to estimate and predict the chlorophyll-a concentration of the whole lake in Taihu.

Key words: Chlorophyll-a, Principal component analysis, BP neural network, Sensitivity analysis

WANG Xue-lian, SONG Yu-zhi, KONG Fan-fan, WANG Yu-jia. Estimation of Chlorophyll-a Concentration in Taihu Lake by Using Back Propagation (BP) Neural Network Forecast Model[J]. Chinese Journal of Agrometeorology, 2016, 37(04): 408-414.

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