Method to predict key factors affecting lake eutrophication – A new approach based on Support Vector Regression model

• SVR was used to identify the influence of environmental factors on eutrophication indices–Chl-a, TN and TP.
• The performance of SVR was more accurate than that of back propagation artificial neural network (BP-ANN).
• SVR model was applied on one lake in China to predict eutrophication.
• SVR model is beneficial to facilitate the establishment of lake eutrophication warning mechanism.

Developing quantitative relationship between environmental factors and eutrophic indices: chlorophyll-a (Chl-a), total nitrogen (TN) and total phosphorus (TP), is highly desired for lake management to prevent eutrophication. In this paper, Support Vector Regression model (SVR) was introduced to fulfill this purpose and the obtained result was compared with previous developed model, back propagation artificial neural network (BP-ANN). Results indicate SVR is more effective for the predication of Chl-a, TN and TP concentrations with less mean relative error (MRE) compared with BP-ANN. The optimal kernel function of SVR model was identified as RBF function. With optimized C and ε obtained in training process, SVR could successfully predict Chl-a, TN and TP concentrations in Chaohu lake based on other environmental factors observation.