Prediction technique for water-bloom in lakes based on elman network

The outbreak of water-bloom is the result of coactions of water body´s physical, chemical, biologic and other progresses. It is very difficult to establish uniform mathematical model to efficiently evaluate and predict the water-bloom because of the water body´s biodiversity and nonlinearity. Based on the foundation of research in the mechanism of water-bloom and the main component analysis in rivers and lakes, modeling method, fundamental theory, principle and technique established for water-bloom prewarning system which is based on artificial neural network is specially researched. Elman network has the characters of good dynamic characteristics, fast approaching speed, high degree of accuracy and so on. In this paper, combined with the basic features of outbreak of water-bloom, Elman network is studied from the angles of theory and experiment and a water-bloom prewarning system in short term based on Elman network is established. Considering the defect of classical BP algorithm, improved BP algorithm is used for the training and study of network. Example analysis shows that Elman network model established in this paper is reliable and practical. Moreover, water-bloom prewarning system in short term in long river water system is also established by MATLAB in this paper which proposes a new method for intelligent research in water-bloom prewarning.