Prediction method of Chlorophyll-a concentration in seawater based on extreme learning machine regression

Effective monitoring the state of chlorophyll-a concentration in seawater plays an important role for the early warning of marine disasters, such as coastal red tides. Grey correlation analysis method is used to determine the input variables of the prediction model. It can effectively reduce the dimension of the model system. Extreme learning machine regression (ELMR) method was used to build the prediction model of chlorophyll-a concentration in seawater. Comparing with the generalized regression neural network and support vector machine regression model, it indicates that extreme learning machine regression has better accuracy, efficiency and generalization ability of prediction than other methods. It adapts to be used for predicting the concentration of chlorophyll-a in this researched seawater area.