Comparing deep learning with several typical methods in prediction of assessing chlorophyll-a by remote sensing: a case study in Taihu Lake, China

Chlorophyll-a (Chl-a) is an important index in water quality assessment by remote sensing technology. For the study of Chl-a value measurement in rivers or lakes, there were many classical methods, such as curve fitting, back propagation (BP) neural network and radial basis function (RBF) neural network, and all of them had some corresponding applications. With the rise of computer power and deep learning, this study intended to analyze the measurement of water quality and Chl-a in deep learning (DL) and to compare with several classical methods, so as to explore and develop better methods. Taking Taihu Lake of China as the case, this study adopted the measured data of Chl-a in Taihu Lake in 2017 and the data corresponding to the same time of Landsat8. In this study, the four methods were used to inverse the distribution of Chl-a value in Taihu Lake. From the results of inversion, power curve fitting model with the n-ary sumation Residual(2) of fitting of 90.469 and inverse curve fitting model with the one of 602156.608 had the better results than other curve fitting models, however, were not as accurate as the machine learning method from segmentation results images. The machine learning method had better accuracy than the curve fitting methods from segmentation results images. The mean squared error of testing of the three methods of machine learning (BP, RBF, DL) were respectively 1.436, 4.479, 4.356. Thus, the BP method and DL method had better results in this study.