有效监测海水Chl a浓度状况对近海赤潮等海洋灾害的预警预报有着重要意义。运用灰色关联分析法确定预测模型的输入变量,可有效降低预测模型系统维数。采用极限学习机回归方法建立海水Chl a浓度预测模型,通过与广义回归神经网络、支持向量机回归二种模型的预测效果进行对比,表明极限学习机回归预测模型具有较好的预测精度、预测效率和泛化能力,能够实现针对研究水域环境下Chl a浓度的有效预测。 Effective monitoring of Chl a concentration in seawater is of great significance for early warning and forecasting of marine disasters such as coastal red tides. Using gray relational analysis to determine the input variables of the forecasting model can effectively reduce the dimensionality of the forecasting model system. The limit learning machine regression method was used to establish the seawater Chl a concentration prediction model. Compared with the general regression neural network and SVM regression model, the prediction results of the two models showed that the prediction model of the limit learning machine regression model has better prediction accuracy and predictive efficiency And generalization ability, can effectively predict Chl a concentration in the study waters.