Abstract
Algal bloom is a nonlinear and time-varying process, which brings challenges for the accurate prediction. For the existing mechanism model of algae ignores the external key factors, we propose an algae growth model (AGM) optimized by action dependent heuristic dynamic programming (ADHDP). This model has the structure of information interaction with the outside, which can predict algal bloom with well adaptive ability. In this paper, chlorophyll-a concentration is used as the representative factor of algal bloom. We use ADHDP approach to map the external key factors to the time-varying parameters, so the AGM can be adjusted to realize the self-adaptive prediction with the changes in external environments. Compared with different prediction methods, the simulation result shows that the ADHDP-AGM prediction model can accurately predict the chlorophyll-a concentration under different data distributions. Moreover, the prediction process shows that the time-varying parameters in AGM conform to the evolution trend of chlorophyll-a concentration in fact, which further improves the interpretability of prediction model. It provides a new perspective for building a data-driven prediction model with clear physical significance, and makes the mechanism research and data science further fusion.
Highlights
In the past, for the lack of the environmental awareness, the eutrophication of lake and reservoir has become increasingly severe with the development of global industrial technology [1]–[3]
TRAINING AND TESTING PHASE As shown in Figure 5, the action dependent heuristic dynamic programming (ADHDP)-algae growth model (AGM) prediction model proposed in this paper focuses on the off-line training of ADHDP structure, and the post-trained action network combined with the AGM can be used for short-term prediction of algal bloom, that is the gray area after training in Figure 2 can be used as the prediction model
The Chl-a concentration prediction result is compared with the LSTM, back propagation neural network (BPNN), and particle swarm optimization (PSO)-AGM prediction model, respectively
Summary
For the lack of the environmental awareness, the eutrophication of lake and reservoir has become increasingly severe with the development of global industrial technology [1]–[3]. H. Zhang et al.: ADHDP Approach for Algal Bloom Prediction With Time-Varying Parameters of dynamic variables estimation. For the prediction of algal bloom in the water, the ecological model has a more in-depth description on the mechanism [25]–[27] It focuses on the complex relationship between ecological factors in a specific environment, so the model needs too many parameters to start (Figure 1). The novel model has the parameters adjustment ability in time, based on internal and external information To construct this complex mapping relationship, an ADHDP approach [29], [30] is selected. The action network is used to map the nonlinear relationship, the critic network is used to evaluate the prediction error, and guide the training of action network This approach can solve the identification problem of time-varying parameters.
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