Optimization of deep learning model for coastal chlorophyll a dynamic forecast

Ding Wenxiang, Zhang Caiyun, Shang Shaoping and Li Xueding

Ecological Modelling, 2022, vol. 467, issue C

Abstract: Chlorophyll a is an important factor in characterizing algal biomass. Its dynamic forecast model is considered to be one of the best early warning methods to prevent or alleviate the occurrence of algal blooms. In this study, the absolute concentration of Chlorophyll (Chl), the change rate of Chl (ΔChl), and the relative change rate of Chl (ΔRChl) were used as the output of a long short-term memory (LSTM) model. The model was used to carry out Chl dynamic forecasts for different seasons in Xiamen Bay. The results show that the Chl forecast result obtained using ΔChl and ΔRChl is much better than the forecast using Chl. Combining the Chl forecast results obtained using ΔChl and ΔRChl can solve the problem of overestimating the Chl high value, thereby improving the forecasting accuracy. Effectively applying our understanding of the mechanisms of deep learning forecasting models can improve forecasting capabilities.

Keywords: Chlorophyll dynamic forecast; Deep learning; Relative change rate; Xiamen Bay (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:467:y:2022:i:c:s0304380022000370

DOI: 10.1016/j.ecolmodel.2022.109913

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