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 are many classical methods, such as curve fitting, back propagation (BP) neural network and radial basis function (RBF) neural network, and all of them have 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 it 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 from Landsat8. In this study, the four methods were used to invert the distribution of the Chl-a value in Taihu Lake. From the results of inversion, the power curve fitting model with ∑Residual2 of fitting of 90.469 and inverse curve fitting model with the ∑Residual2 of fitting of 602,156.608 had better results than the other curve fitting models; however, they 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. HIGHLIGHTS BP had the best results, and DNN also had nice mean squared error of results.; In the future, the data volume and equipment condition need to be discussed to choose the optimal method.; The machine learning method had less error than the curve fitting methods.