posted on 2024-03-04, 15:29authored byJing Qian, Li Qian, Nan Pu, Yonghong Bi, Andre Wilhelms, Stefan Norra
Harmful algal blooms (HABs) pose
a significant ecological
threat
and economic detriment to freshwater environments. In order to develop
an intelligent early warning system for HABs, big data and deep learning
models were harnessed in this study. Data collection was achieved
utilizing the vertical aquatic monitoring system (VAMS). Subsequently,
the analysis and stratification of the vertical aquatic layer were
conducted employing the “DeepDPM-Spectral Clustering”
method. This approach drastically reduced the number of predictive
models and enhanced the adaptability of the system. The Bloomformer-2
model was developed to conduct both single-step and multistep predictions
of Chl-a, integrating the ” Alert Level Framework” issued
by the World Health Organization to accomplish early warning for HABs.
The case study conducted in Taihu Lake revealed that during the winter
of 2018, the water column could be partitioned into four clusters
(Groups W1–W4), while in the summer of 2019, the water column
could be partitioned into five clusters (Groups S1–S5). Moreover,
in a subsequent predictive task, Bloomformer-2 exhibited superiority
in performance across all clusters for both the winter of 2018 and
the summer of 2019 (MAE: 0.175–0.394, MSE: 0.042–0.305,
and MAPE: 0.228–2.279 for single-step prediction; MAE: 0.184–0.505,
MSE: 0.101–0.378, and MAPE: 0.243–4.011 for multistep
prediction). The prediction for the 3 days indicated that Group W1
was in a Level I alert state at all times. Conversely, Group S1 was
mainly under an Level I alert, with seven specific time points escalating
to a Level II alert. Furthermore, the end-to-end architecture of this
system, coupled with the automation of its various processes, minimized
human intervention, endowing it with intelligent characteristics.
This research highlights the transformative potential of integrating
big data and artificial intelligence in environmental management and
emphasizes the importance of model interpretability in machine learning
applications.