Inversion of chlorophyll-a concentration in Donghu Lake based on machine
Machine learning algorithm, as an important method for numerical
modeling, has been widely used for chlorophyll-a concentration inversion
modeling. This work aims to build an effective inversion model of
chlorophyll-a concentration in Lake Donghu based on machine learning
algorithm. Toward this aim, a variety of models were built by applying
five kinds of dataset and adopting back propagation neural network
(BPNN), extreme learning machine (ELM), support vector machine (SVM).
The model accuracy analysis results revealed that multi-factor dataset
for modeling has the possibility to improve the accuracy of the
single-factor 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.
SVM3 is the best inversion one among all multi-factor models that the
MRE, MAE, RMSE of SF-SVM are 30.82%, 9.44 μg/L and 12.66 μg/L,
respectively. SF-SVM performs a better inversion effect than SF-BPNN and
SF-ELM, the MRE, MAE, RMSE of SF-SVM are 28.63%, 13.69μg/L and
16.49μg/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.