Paper Info
Title
Harmful algal bloom warning based on machine learning in maritime site monitoring
Abstract
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.
Year
DOI
Venue
2022
10.1016/j.knosys.2022.108569
Knowledge-Based Systems
Keywords
DocType
Volume
Harmful Algal bloom forecasting,Machine learning,Time series analysis,Ocean environment model,LSTM network
Journal
245
ISSN
Citations 
PageRank 
0950-7051
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Jiabao Wen1164.61
Jiachen Yang200.68
Yang Li301.01
Liqing Gao400.34