The Method of Soft Sensing for Water Bloom in River and Lakes Based on RBF Neural Network

After the major elements of water bloom were analyzed, a method to confirm dominant variables of soft sensing and shortdated forecast model for water bloom, and a supervise learning method to determine the clustering center, extent and weighting function value of radial base function were studied, then the soft sensing method based on RBF neural network for water bloom was proposed, The fitting ability and extensive capability of neural network are discussed according to different nodes of hidden layer and the extent of RBF neural network. The results of neural network training and water bloom forecast show that the soft sensing model based on RBF neural network possess strong extensive capability and forecast precision, and it can provide a new method and research groundwork for the forecast of water bloom in river and lakes.