GA-SVM Applied in Assessing the Water Trophic State of South Lake Qujiang based on Multispectral RS

Abstract

Eutrophication has become a major water quality problem in most urban landscape waters of the world. Despite extensive research over the last four to five decades, many of the key issues in eutrophication science remain unsolved. In this paper, based on Support Vector Machine (SVM) a new method was proposed to monitor and evaluate the water trophic state of Qujiang South Lake. SVM is suitable for a limited number of samples because of strong nonlinear mapping ability. Model parameters can be automatically chosen by Genetic Algorithm (GA) which contributes to advantages of the Genetic Algorithm- Support Vector Machine (GA-SVM) which has high precision in solving regression problems. Enhanced Thematic Mapper (ETM) data can be used to estimate the chlorophyll-a (Chl-a) concentration of the water body. The characteristic band ratio and SVM method are used to establish a model of Chl-a concentration through remote sensing. The comprehensive eutrophication condition can be evaluated by the remote sensing (RS) results. Results show that the prediction accuracy of the GA-SVM method is better than the retrieval results of the traditional statistical regression method and a neural network. Besides, RS retrieval results corresponded with the in situ measured values, indicating that the GA-SVM is effective. Furthermore, RS data can be free downloaded, so it is also economical than in situ measuring methods. The GA-SVM can also be used to assessment larger lake eutrophication.