Intelligent technology for predicting water bloom engendering
Main factors which make water bloom engendering in river and lakes is analyzed, and the modeling method of short-time predicting for water bloom based on RBF neural network, including supervise learning method for the center, width and weight of base function in RBF neural network, error-correction algorithm based on gradient descent of RBF, is proposed. The effect which hidden layer of RBF brings to network performance is compared, and fitting capacity between RBFpsilas width and generalization capability of network is discussed. According to the results of network training and water bloom forecast, RBF neural network can be used to forecast the change of Chi_a in short term. Because of the strong generalization capability, high forecast precision and good fitting performance, the model has established a solid foundation for further research on short-term forecast methods of water bloom in river and lakes and the simulation result showed that the method is very practice and useful.