Paper Info
Title
An Action Dependent Heuristic Dynamic Programming Approach For Algal Bloom Prediction With Time-Varying Parameters
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.
Year
DOI
Venue
2020
10.1109/ACCESS.2020.2971244
IEEE ACCESS
Keywords
DocType
Volume
Algal bloom, action dependent heuristic dynamic programming, time-varying parameters identification, prediction
Journal
8
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Huiyan Zhang101.35
Bo Hu200.34
Xiaoyi Wang33716.96
Jiping Xu435.50
LiMin Wang55910.33
Qian Sun652.46
Zhiyao Zhao7165.45