Eutrophication has become a serious environment problem in many parts of the world and Chlorophyll-a concentration is one of the important parameters for the characterization of water quality, which reflects the degree of eutrophication and algae content in the water body. So establishing a forecasting model to predict the chlorophyll-a concentration in evaluation of eutrophication become more urgent. In this paper, a hybrid model of least squares support vector regression optimized by improved particle swarm optimization and radial basis function neural networks (IPSO-LSSVR-RBFNN) was proposed, which effectively modifying the forecasting accuracy by extracting the useful information in the error term of the traditional methods. A real monthly dataset that collected from a typical reservoir in China during 2010–2012 and two public datasets were used to evaluate the performance of the proposed hybrid model. From the experiment results, we can see that the proposed model of IPSO-LSSVR-RBFNN achieve a higher accuracy rate compared with other models.