A new technique for rapid assessment of eutrophication status of coastal waters using a support vector machine

Kong, Xianyu; Che, Xiaowei; Su, Rongguo; Zhang, Chuansong; Yao, Qingzhen; Shi, Xiaoyong

doi:10.1007/s00343-017-6224-0

Cited by 10 publications

(1 citation statement)

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“…Support vector machine (SVM) was proposed by Cortes and Vapnik in 1995 [55]. It can be used for classification [56,57] as well as regression [58]. It has an advantages in establishing small sample, nonlinear and high dimensional model, and it can be extended to other machine learning problems such as function fitting [59].…”

Section: Support Vector Machinementioning

confidence: 99%

Inversion of Chlorophyll-a Concentration in Donghu Lake Based on Machine Learning Algorithm

Tang

Huang

2021

Water

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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.

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