Short-term predicting model for water bloom based on Elman neural network

This paper addresses the problem of predicting water bloom in short-term period. Important factors of water bloom are studied. A short-term predicting model of Elman neural network is presented according to the characteristic of time accumulation. The algorithm of Elman is first improved, and then the predicting model is trained, tested and compared with BP model. Experimental results show that: The short-term change of chlorophyll could be predicted better by Elman predicting model, which is accurate and extensive. This model is proven to be useful to predict water bloom in short-term period.