An approach of multi-element fusion method for harmful algal blooms prediction

Chen, Xiaoqian; Fu, Yonggang; Zhou, Honghua

doi:10.1007/s11356-022-23944-3

Cited by 1 publication

(1 citation statement)

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“…To promote the monitoring and prediction of harmful algal blooms, a multi-element fusion prediction (MEFP) method for cyanobacteria bloom was proposed (X. Chen et al, 2022). Tang et al created a multisource hybrid dataset for deep learning model training to predict harmful algal bloom events in Lake Okeechobee (Tang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

An early warning model for starfish disaster based on multi-sensor fusion

Liu

Hui

et al. 2023

Front. Mar. Sci.

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Starfish have a wide range of feeding habits, including starfish, sea urchins, sea cucumbers, corals, abalones, scallops, and many other marine organisms with economic or ecological value. The starfish outbreak in coastal areas will lead to severe economic losses in aquaculture and damage the ecological environment. However, the current monitoring methods are still artificial, time-consuming, and laborious. This study used an underwater observation platform with multiple sensors to observe the starfish outbreak in Weihai, Shandong Province. The platform could collect the temperature, salinity, depth, dissolved oxygen, conductivity, other water quality data, and underwater video data. Based on these data, the paper proposed an early warning model for starfish prevalence (EWSP) based on multi-sensor fusion. A deep learning-based object detection method extracts time-series information on the number of starfish from underwater video data. For the extracted starfish quantity information, the model uses the k-means clustering algorithm to divide the starfish prevalence level into four levels: no prevalence, mild prevalence, medium prevalence, and high prevalence. Correlation analysis concluded that the water quality factors most closely related to the starfish prevalence level are temperature and salinity. Therefore, the selected water quality factor and the number of historical starfish are inputted. The future starfish prevalence level of the starfish outbreak is used as an output to train the BP (back propagation) neural network to build EWSP based on multi-sensor fusion. Experiments show that the accuracy rate of this model is 97.26%, whose precision meets the needs of early warning for starfish outbreaks and has specific application feasibility.

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Section: Introductionmentioning

confidence: 99%