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

Wen, Jiabao; Yang, Jiachen; Li, Yang; Gao, Liqing

doi:10.1016/j.knosys.2022.108569

Cited by 15 publications

(5 citation statements)

References 33 publications

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“…Aside from temporal continuity, spatial dependency also exists in the lake HAB forecasting due to the fluidity of water and the interchange of pollutant. 28 Generally, three types of spatial dependencies exist in the river-lake system: (i) river-toriver (downstream of the river is affected by conditions of upstream), (ii) river-to-lake (lake is affected by inflow rivers), and (iii) lake-to-lake (one lake station is affected by nearby lake stations). These spatial relationships contain important information for HAB interpreting and forecasting.…”

Section: Introductionmentioning

confidence: 99%

Mining Spatiotemporal Information for Harmful Algal Bloom Forecasting and Mechanism Interpreting

Jia,

Xu,

Jia

et al. 2024

ACS EST Water

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A multistep spatiotemporal forecasting (MSTF) network is developed through incorporating the graph convolutional network (GCN) and the long short-term memory (LSTM) network within a sequence-to-sequence (seq2seq) framework. The MSTF method can not only extract spatial and temporal information from the input data but also make multistep-ahead and continuous predictions. An MSTF-based harmful algal bloom (HAB) forecasting model is then formulated to predict the chlorophyll-a (Chl-a) concentration of the Dianchi Lake (China). The integrated gradients (IG) method is employed to interpret the trained MSTF model and quantify the attribution of each input dimension to the Chl-a prediction. Results indicate that (i) the coefficient of determination (R 2) of the MSTF model in 24-h-ahead Chl-a prediction reaches 0.926, 28.4% higher than that of the traditional LSTM model; (ii) the ammonia nitrogen (12.3%), the total phosphorus (10.2%), the total nitrogen (9.9%), and the temperature (8.6%) are significant variables for Chl-a prediction; (iii) the spatial information from neighbor lake and river stations plays an important role in the HAB forecasting, with an average contribution of 35.0%; (iv) the proposed MSTF model is also skillful in the 72-h-ahead Chl-a prediction. Results presented highlight the importance of considering both spatial and temporal dependency of monitoring data in HAB forecasting and mechanism interpreting.

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Section: Introductionmentioning

confidence: 99%

Mining Spatiotemporal Information for Harmful Algal Bloom Forecasting and Mechanism Interpreting

Jia,

Xu,

Jia

et al. 2024

ACS EST Water

View full text Add to dashboard Cite

show abstract

“…When the target point or obstacles change slightly, these methods need to re-plan because of the lack of adaptability and flexibility. With the rapid development of deep learning in recent years, intelligent methods have played an increasingly important role in smart agriculture [15,16], image screening [17], environmental monitoring [18], edge computing [19], and path planning [20]. At the same time, it provides an effective technical means for agricultural production, automatic driving, and other fields.…”

Section: Introductionmentioning

confidence: 99%

The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning

Yang

Chen

2022

Sensors

Self Cite

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Agricultural robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology and the maturity of Internet of Things (IoT) technology, people put forward higher requirements for the intelligence of robots. Agricultural robots must have intelligent control functions in agricultural scenarios and be able to autonomously decide paths to complete agricultural tasks. In response to this requirement, this paper proposes a Residual-like Soft Actor Critic (R-SAC) algorithm for agricultural scenarios to realize safe obstacle avoidance and intelligent path planning of robots. In addition, in order to alleviate the time-consuming problem of exploration process of reinforcement learning, this paper proposes an offline expert experience pre-training method, which improves the training efficiency of reinforcement learning. Moreover, this paper optimizes the reward mechanism of the algorithm by using multi-step TD-error, which solves the probable dilemma during training. Experiments verify that our proposed method has stable performance in both static and dynamic obstacle environments, and is superior to other reinforcement learning algorithms. It is a stable and efficient path planning method and has visible application potential in agricultural robots.

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“…With the development of Internet of things technologies [1,2], deep learning [3][4][5], and communication technologies [6,7], wearable devices and intelligent terminal devices have developed rapidly, which have brought unprecedented changes to many fields. Intelligent medical systems, intelligent industrial systems, and smart home systems are changing people's lifestyles.…”

Section: Introductionmentioning

confidence: 99%

Enriching Facial Anti-Spoofing Datasets via an Effective Face Swapping Framework

Lan

Xiao

et al. 2022

Sensors

Self Cite

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In the era of rapid development of the Internet of things, deep learning, and communication technologies, social media has become an indispensable element. However, while enjoying the convenience brought by technological innovation, people are also facing the negative impact brought by them. Taking the users’ portraits of multimedia systems as examples, with the maturity of deep facial forgery technologies, personal portraits are facing malicious tampering and forgery, which pose a potential threat to personal privacy security and social impact. At present, the deep forgery detection methods are learning-based methods, which depend on the data to a certain extent. Enriching facial anti-spoofing datasets is an effective method to solve the above problem. Therefore, we propose an effective face swapping framework based on StyleGAN. We utilize the feature pyramid network to extract facial features and map them to the latent space of StyleGAN. In order to realize the transformation of identity, we explore the representation of identity information and propose an adaptive identity editing module. We design a simple and effective post-processing process to improve the authenticity of the images. Experiments show that our proposed method can effectively complete face swapping and provide high-quality data for deep forgery detection to ensure the security of multimedia systems.

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

Cited by 15 publications

References 33 publications

Mining Spatiotemporal Information for Harmful Algal Bloom Forecasting and Mechanism Interpreting

Mining Spatiotemporal Information for Harmful Algal Bloom Forecasting and Mechanism Interpreting

The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning

Enriching Facial Anti-Spoofing Datasets via an Effective Face Swapping Framework

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