Harmful algal bloom warning based on machine learning in maritime site monitoring

Forecasting harmful algal blooms (HABs) is an important part of marine environmental monitoring. Algae bloom observation channels includes satellite remote sensing and maritime station monitoring (MSM). Compared with satellite remote sensing image, MSM can collect more accurate data, which includes various seawater inorganic salt content related to the HABs. However, the measured data of MSM is easily affected by regional sediment content and seawater dynamic field, which leads to lack the accurate forecasting models. Therefore, this paper proposed a local spatio-temporal HABs forecasted model (STHFM) based on few impact factors in MSM. The advantage of this model is to first use principal component analysis to select main environment factors (MEFs) related to HABs. Then, this model distinguishes multiple warning levels of HABs in spatio-temporal according to the algae growth rate. Finally, this paper generates continuous MEFs time series information based on the Autoregressive Integrated Moving Average (ARIMA) model in the high-level warning area. And an improved LSTM network with MEFs time series as input is established to forecast HABs in future. The proposed model is tested on the NOAA website public dataset, which contains historical harmful algae Alexandrium data on the East Coast of US. The experimental shows that our model has good HABs monitoring performance. Under the public NOAA Alexandrium dataset, the proposed model can achieve the highest prediction accuracy of 82.1%, and has a small prediction error.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this paper, a local spatio-temporal HABs forecasted model based on MSM is proposed, which uses principal component analysis to select main environment factors related to HABs, distinguishes different warning levels based on the algae growth rate, generates continuous time series information with an ARIMA model, and establishes an improved LSTM network for HABs forecasting.