Water Quality, Exposure and Health

, Volume 7, Issue 4, pp 591–602 | Cite as

Developing a PCA–ANN Model for Predicting Chlorophyll a Concentration from Field Hyperspectral Measurements in Dianshan Lake, China

  • Liguo Zhou
  • Weichun Ma
  • Hao ZhangEmail author
  • Linna Li
  • Lin Tang
Original Paper


This paper aims at combining principle component analysis (PCA) and artificial neural network (ANN) algorithm to predict chlorophyll a concentration in Dianshan Lake, Shanghai, eastern China. Firstly, based on field hyperspectral measurements, the sensitive wavelengths were selected as the input variables to build the basic ANN model, and the estimate accuracy (R 2) reached 0.85. In order to improve the accuracy and stability of the ANN model, the total nitrogen, total phosphorus, chemical oxygen demand, dissolve oxygen, and dissolved potential of hydrogen were selected as additional input variables. Consequently, the model accuracy increased to 0.9091. Further, aiming at eliminating the effect of inter-correlation of input variables, the PCA method was utilized to reduce the dimension of input variables. The result shows that the combined PCA–ANN model can reach an estimated accuracy with R 2 = 0.9184 and RMSE < 5.6 mg m−3. Moreover, the stability and performance of the enhanced model was further evaluated by cross-validation of PCA–ANN model output and in situ measured datasets. The model sensitivity test through adding 10 % Gauss white noise to the input variables also proved that the enhanced PCA–ANN model has better noise tolerance ability.


Hyperspectral Artificial neural network (ANN) Principle component analysis (PCA) Chlorophyll a Dianshan Lake 



This work was jointly funded by National Natural Science Foundation of China (Grant No. 41001234 and 41171432), Shanghai Municipal Natural Science Foundation of China (Grant No. 15ZR1404000), and specialized Research Fund for the Doctoral Program of Higher Education of China (2010007120013). The authors are grateful to the anonymous reviewers for their valuable comments on this manuscript.


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Liguo Zhou
    • 1
  • Weichun Ma
    • 1
  • Hao Zhang
    • 1
    Email author
  • Linna Li
    • 2
  • Lin Tang
    • 3
  1. 1.Department of Environmental Science and EngineeringFudan UniversityShanghaiChina
  2. 2.Department of Geography & Environmental StudiesCalifornia State UniversitySan BernardinoUSA
  3. 3.Shanghai Environmental Monitoring CenterShanghaiChina

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