Prediction method of cyanobacterial blooms spatial-temporal sequence based on deep belief network and fuzzy expert system

Abstract

The process of cyanobacteria bloom in rivers and lakes is a highly non-stationary and non-linear process. The existing cyanobacterial bloom prediction method mainly uses time series model and single intelligent model, but time series model and single intelligent model cannot effectively explain the cyanobacterial bloom generation process, and the prediction accuracy is not high. In view of the above deficiencies, this paper proposes to use the cyanobacteria bloom spatiotemporal sequence data for modeling. Considering the characteristics of large-scale nonlinear trend term and small-scale residual term in the cyanobacteria bloom spatial-temporal sequence, the deep belief networks is used to model and explain the large-scale nonlinear trend term of the cyanobacteria bloom spatiotemporal sequence. Then use the time autocorrelation model and the multivariate spatiotemporal autocorrelation model to model and interpret the small-scale residual term; finally, after superimposing the large-scale nonlinear trend term and the small-scale residual term, the adaptive neuro-fuzzy system model is used to predict the chlorophyll a value of the water. Therefore, a fuzzy spatial and temporal sequence prediction method based on fuzzy expert system is proposed. The model verification results show that compared with the existing time series model and single intelligent model, the method can more fully explain the non-stationary and nonlinear dynamic changes of the cyanobacterial bloom spatial-temporal sequence. It provides a new method for accurately predicting cyanobacteria bloom in rivers and lakes.