Video surveillance on the offshore booster station and around the coast is a effective way to monitor floating macroalgae. Previous studies on floating algae detection are mainly based on traditional image segmentation methods. However, these algorithms cannot effectively solve the problem of extracting Ulva prolifra and Sargassum at different sizes and views. Recently, instance segmentation methods have achieved great success in computer vision applications. In this paper, based on the CenterMask network, a novel instance segmentation architecture named AlgaeMask is proposed for floating algae detection from the surveillance videos. To address the feature extraction ability of the network in the inter-dependencies for position and channel, we introduce a new OSA-V3 module with the dual-attention block, which consists of a position attention mechanism and channel attention mechanism. Meanwhile, scale-equalizing pyramid convolution is introduced to solve the problem of scale difference. Finally, we introduce the feature decoder module based on FCOS head and segmentation head to obtain the segmentation area of floating algae in each bounding box. The extensive experiment results show that the average precision of our AlgaeMask in the tasks of mask segmentation and box detection can reach 44.22% and 48.13%, respectively, which has 15.09% and 8.24% improvement over CenterMask. In addition, the AlgaeMask can meet the real-time requirements of floating algae detection.

The accurate temperature background field plays a vital role in the numerical prediction of sea surface temperature (SST). At present, the SST background field is mainly derived from multi-source data fusion, including satellite SST data and in situ data from marine stations, buoys, and voluntary observing ships. The characteristics of satellite SST data are wide coverage but low accuracy, whereas the in situ data have high accuracy but sparse distribution. For obtaining a more accurate temperature background field and realizing the fusion of measured data with satellite data as much as possible, we propose a sparse data-extended fusion method to predict SST in this paper. By using this method, the actual observed sites and buoys data in the East China Sea area are fused with Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Version 5.0 SST data. Furthermore, the temperature field in the study area were predicted by using Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) deep learning methods, respectively. Finally, we obtained the results by traditional prediction methods to verify them. The experimental results show that the method we proposed in this paper can obtain more accurate prediction results, and effectively compensate for the uncertainty caused by the parameterization of ocean dynamic process, the discrete method, and the error of initial conditions.

In a marine environment, the concentration of chlorophyll is an important indicator of quality, which is also considered an indicator used to predict the marine ecological environment, which is further considered an important means of predicting red tide disasters. Although existing methods for predicting chlorophyll concentration have achieved encouraging performance, there are still two limitations: (i) they primarily focus on the correlation between variables while ignoring negative noise from non-predictive variables and (ii) they are unable to distinguish the impact of chlorophyll from that of non-predictive variables on chlorophyll concentration at future time points. In order to overcome these obstacles, we propose a Multi-Attention Collaborative Network (MACN)-based triangle-structured prediction system. In particular, the MACN consists of two branch networks, with one named NP-net, focusing on non-predictive variables, and the other named T-net, applied to the target variable. NP-net incorporates variable-distillation attention to eliminate the negative effects of irrelevant variables, and its outputs are used as auxiliary information for T-net. T-net works on the target variable, and both its encoder and decoder are related to NP-net to use the output of NP-net for assistance in learning and prediction. Two actual datasets are used in the experiments, which show that the MACN performs better than various kinds of state-of-the-art techniques.

Green tide is a harmful marine ecological phenomenon caused by the explosive proliferation or high aggregation of some macroalgae, and can cause significant impacts on ecological environments and economies. An effective emergency disposal plan can significantly improve disposal capacity and reduce total costs. At present, the formulation of emergency disposal plans for green tide disasters usually depends on subjective experience. The primary purpose of this paper is to develop a decision-support model based on intelligent algorithms to optimize the type and number of resources when making emergency disposal plans so as to improve the reliability and efficiency of decision making. In order to simulate the decision-making environment more realistically, the drift motion of green tide is considered in this model. Two intelligent algorithms, the Genetic Algorithm (GA) and the improved Non-Dominated Sorting Genetic Algorithm-II (IMNSGA-II), are used to solve the model and find appropriate emergency disposal plans. Finally, a case study on the green tide disaster that occurred in Qingdao (Yellow Sea, China) is conducted to demonstrate the effectiveness and optimization of the proposed model. Through the model proposed in this paper, the overall response time and cost can be reduced in green tide disaster emergency operations.

Multistep-ahead forecasting of chlorophyll concentration is of great significance in red tide early warning systems. Existing methods often neglect the potential adverse interactions between non-predictive variables and chlorophyll while failing to fully utilize the effective information in historical decoder units. To address these issues, this paper proposes a Dynamic Collaborative Attention Network (DCAN) model for chlorophyll concentration forecasting, which consists of two components: a Two-Stage Variable Embedding Network (TSVEN) and a Dynamic Attention Network (DyAN). The TSVEN can identify the non-predictive variables that have the most significant impact on chlorophyll changes and generate corresponding spatial vectors from them, thereby alleviating the information conflict between chlorophyll and non-predictive variables. The DyAN integrates a context attention module and a filtering gate mechanism. The former effectively extends the forecasting time range by dynamically retrieving historical decoder states, while the latter selectively integrates historical decoder information, thereby improving the reliability of model decisions and prediction accuracy. Experimental results based on real datasets show that the proposed model outperforms the current state-of-the-art methods in chlorophyll concentration forecasting tasks and exhibits good interpretability.