[Quantitative retrieval of chlorophyll a concentration in Taihu Lake using machine learning methods].
We evaluated the performance of two machine learning methods, artificial neural net (ANN) and support vector machine (SVM), for estimation of chlorophyll a in Taihu Lake from remote sensing data. The theoretical analysis has been done from basic theory and learning target of these two methods first. Then two empirical algorithms have been developed to relate reflectance of MODIS to in situ concentrations of chlorophyll a. The performance of ANN and SVM is comparatively analyzed in terms of validation, stability and robustness assessment and chlorophyll a distribution of Taihu Lake from two algorithms. The root of mean square deviation (RMSE) and average relative error (ARE) of validation data is only 5.85 and 26.5% of SVM retrieval model, however, RMSE and ARE of ANN model is 13.04 and 46.8%. Stability and robustness assessment suggest that SVM provides the better performance than ANN. And the retrieval results show that the chlorophyll a distribution of the whole lake from two algorithms is similar, however, the chlorophyll a concentration in the eastern region and central region of Taihu Lake is distorted by ANN model because of the limitations, such as learning target setting and over-learning in net construction.
Zhang
Yu-Chao
YC
State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, China. zhangych@nju.edu.cn
Qian
Xin
X
Qian
Yu
Y
Liu
Jian-Ping
JP
Kong
Fan-Xiang
FX
D001185:Artificial Intelligence; D002681:China; D002734:Chlorophyll / Q000032:analysis*; D000077194:Chlorophyll A; D004784:Environmental Monitoring / Q000379:methods; D005618:Fresh Water / Q000032:analysis; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D055095:Optics and Photonics; D014873:Water Pollutants / Q000032:analysis*
D014873:Water Pollutants; D002734:Chlorophyll; D000077194:Chlorophyll A