The results of eutrophication assessment can not only reflect the conditions of water quality and the status of pollution, but also provide some evidence and guidance for eutrophication controls, watershed managements and policy decisions. Essentially, the process of eutrophication assessment could be considered as a multi-index classification problem. However, the relationship between the eco-environmental factors and the eutrophication status is complex, nonlinear and uncertain. In recent years, a variety of artificial intelligent methods have been used for eutrophication assessment, such as fuzzy synthetic evaluation, fuzzy mathematics, grey cluster, grey situation decision, evolutionary algorithm and artificial neural network (ANN). These methods have been playing important roles in eutrophication assessment, but there are still uncertainty and inconsistency in the results. The fuzzy and grey methods have great subjectivity in determining the structures of evaluation functions and the weights of evaluation indexes, the evolutionary algorithm is mainly used for parameter optimization of existing evaluation models; and the ANN has inherent problems such as uncertainty of network structure, potential of local optimum and no guarantee of model generality. As a relatively new machine-learning algorithm, Support Vector Machine (SVM) has shown promising advantages in solving classification problems. The fundamental idea is to use certain kind of kernel functions to map the vectors in lower dimensional space to a higher dimensional space so that these vectors could be linearly classified into two parts, and then conducting a hyperplane that has the largest distance to the nearest vector of any class to make the separation. Although sharing some similarities to ANN in process of model building, SVM has entirely different theoretical basis. Despite its broad applications, the method has been merely used in lake eutrophication assessment.
In this study, an eutrophication assessment model has been built by using the classification algorithm of LIBSVM (a simple, easy-to-use, and efficient software for SVM classification and regression), based on the learning data randomly generated from the existing eutrophication assessment standard for lakes and reservoirs. The model was then applied to the Taihu Lake, where data were collected from 33 monitoring sites during July to September in 2012. The results showed that there are 3 types of trophic status in the Taihu Lake with respect to space. The mesotrophic areas were mainly in the east part of the lake, and light eutrophication areas were mostly located in the central and eastern regions, and the moderate eutrophication areas were largely in the northwestern region. The eutrophication status of the entire lake is about light to moderate eutrophication. By comparing to the results from the well-accepted Linear Interpolation Scoring Method, the consistency is about 78.8%, indicating good effectiveness and applicability of the SVM method, owing to its addition feature of high convergence and generalization.