Abstract
Machine learning algorithm, as an important method for numerical modeling, has been widely used for chlorophyll-a concentration inversion modeling. In this work, a variety of models were built by applying five kinds of datasets and adopting back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM). The results revealed that modeling with multi-factor datasets has the possibility to improve the accuracy of inversion model, and seven band combinations are better than seven single bands when modeling, Besides, SVM is more suitable than BPNN and ELM for chlorophyll-a concentration inversion modeling of Donghu Lake. The SVM model based on seven three-band combination dataset (SVM3) is the best inversion one among all multi-factor models that the mean relative error (MRE), mean absolute error (MAE), root mean square error (RMSE) of the SVM model based on single-factor dataset (SF-SVM) are 30.82%, 9.44 μg/L and 12.66 μg/L, respectively. SF-SVM performs best in single-factor models, MRE, MAE, RMSE of SF-SVM are 28.63%, 13.69 μg/L and 16.49 μg/L, respectively. In addition, the simulation effect of SVM3 is better than that of SF-SVM. On the whole, an effective model for retrieving chlorophyll-a concentration has been built based on machine learning algorithm, and our work provides a reliable basis and promotion for exploring accurate and applicable chlorophyll-a inversion model.
Highlights
Lake eutrophication has become a global common environmental problem [1], which exacerbates the deterioration of the global water environment and the shortage of water resource [2,3]
Our work provided a reliable basis to optimize modeling influence factors based on machine learning algorithm and improve the accuracy of chlorophyll-a concentration inversion model
We firstly find out the seven combinations with the highest correlation with chlorophyll-a in the dual-band combination according to the correlation analysis, and construct a dataset based on the seven combinations and the measured data of the chlorophyll-a concentration, which is called the dual-band combination dataset
Summary
Lake eutrophication has become a global common environmental problem [1], which exacerbates the deterioration of the global water environment and the shortage of water resource [2,3]. Chlorophyll-a concentration is an index for estimating primary productivity and biomass in lake ecosystem [4,5], and it is an important indicator of lake eutrophication [6,7]. It is of great signification to monitor the concentration of chlorophyll-a in lake water. The traditional method of monitoring chlorophyll-a concentration is based on a suite of laboratory and situ measures [8], which is time-consuming, costly and regional limited [9]. With the mature development of remote sensing technology and deepening research on spectral characteristics of water quality parameters, remote sensing inversion has become an economical and effective method for real-time and continuous monitoring of chlorophylla concentration in lakes [10,11,12]. Because chlorophyll-a possesses this unique optical property, the inversion model can be built by analysing the statistical relationship between the concentration of chlorophyll-a and the characteristic bands of remote sensing, and the chlorophyll-a concentration of the whole water body is calculated [15]
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