Long-term prediction of sea surface chlorophyll-a concentration based on the combination of spatio-temporal features

Na, Liu; Chen, Shaoyang; Cheng, Zhizhong; Wang, Xing; Xiao, Yun; Li, Xiao; Gong, Yungui; Wang, Tingting; Zhang, Xuefeng; Liu, Siqi

doi:10.1016/j.watres.2022.118040

Cited by 25 publications

(14 citation statements)

References 60 publications

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“…Moreover, water depth was considered a critical factor influencing algal biomass and growth, 8,9 but most studies on HABs early warning systems predominantly only rely on Chl-a data derived from the water's surface. 10 Additionally, the entire process of algal blooms is complex, intertwined with physical, chemical, biological, and hydrological factors. 11 Hence, the development of a new early warning system capable of collecting multiwater-quality-parameter data with high temporal resolution and depth dimension is essential.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Currently, many scholars have developed predictive models using Chl-a data at various temporal resolutions, such as monthly, , semimonthly, weekly, and daily; ,, however, predictive models at higher temporal resolutions, like minute-level, are rare. Moreover, water depth was considered a critical factor influencing algal biomass and growth, , but most studies on HABs early warning systems predominantly only rely on Chl-a data derived from the water’s surface . Additionally, the entire process of algal blooms is complex, intertwined with physical, chemical, biological, and hydrological factors .…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Early Warning System for Harmful Algal Blooms: Harnessing the Power of Big Data and Deep Learning

Qian,

et al. 2024

Environ. Sci. Technol.

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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.

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Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Intelligent Early Warning System for Harmful Algal Blooms: Harnessing the Power of Big Data and Deep Learning

Qian,

et al. 2024

Environ. Sci. Technol.

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“…Harmful algal blooms (HABs) represent a deleterious ecological phenomenon stemming from the explosive proliferation of phytoplankton or bacteria in marine or brackish waters, which disrupt the delicate balance of ecosystems, negatively affect fisheries, and release toxic substances harmful to human health [1][2][3]. Over the past 30 years, HAB events have shown a significant increase in frequency, scale, and geographical distribution, evolving into a worldwide ecological problem [4]. Therefore, the monitoring of HABs has been a topic of concern for marine environmental scientists in the past several decades [5].…”

Section: Introductionmentioning

confidence: 99%

“…Instead, satellite remote sensing technology, with its enhanced spatiotemporal coverage, has greatly advanced our comprehension of nearsurface ocean phenomena and played an important role in supporting the monitoring of aquatic-related processes, especially HABs [13,14]. Therefore, a large amount of work based on remote sensing products has been conducted to explore chl-a, the important water quality variable, and carry out research on concentration inversion algorithms [15][16][17][18][19], environmental factors influencing mechanisms [20,21], trend prediction [4,22,23], and other aspects to assist HABs monitoring and prevention.…”

Section: Introductionmentioning

confidence: 99%

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

Hong,

Qin,

2024

Applied Sciences

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Chlorophyll-a (chl-a) serves as a key indicator in water quality and harmful algal blooms (HABs) research. While satellite ocean color data have greatly advanced chl-a research and HABs monitoring, missing data caused by cloud cover and other factors limit the spatiotemporal continuity and the utility of remote sensing data products. The Data Interpolating Empirical Orthogonal Function (DINEOF) method, widely used to reconstruct missing values in remote sensing datasets, is open to improvement in terms of computational accuracy and efficiency. We propose an improved method called Concentration-Stratified DINEOF (CS-DINEOF), which uses a coordinate–value correlative data division strategy to stratify the study area into several subregions based on annual average chl-a concentration. The proposed method clusters data points with similar spatiotemporal patterns, allowing for more targeted and effective reconstruction in each sub-dataset. The feasibility and advantage of the proposed method are tested and evaluated in the experiments of chl-a data reconstruction in the water of the Bohai Sea. Compared with the ordinary DINEOF method, the CS-DINEOF method improves the reconstruction accuracy, with an average Root Mean Square Error (RMSE) reduction of 0.0281 mg/m3, and saves computational time by 228.9%. Furthermore, the gap-free images generated from CS-DINEOF are able to illustrate small variations and details of the chl-a distribution in local areas. We can conclude that the proposed CS-DINEOF method is superior in providing significant insights for water quality and HABs studies in the Bohai Sea region.

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“…Machine learning can solve complex problems that are difficult with traditional methods. Deep learning is a special machine learning, which has been applied to the marine field in recent years, such as predicting sea surface temperature [20], wave height [21], and sea surface Chl-a concentrations [12,[22][23][24]. Many current research efforts have successfully predicted Chl-a using machine learning models, including Random Forest (RF) [12], Support Vector Regression (SVR) [23], Convolutional Neural Networks (CNN) [24,25], and Recurrent Neural Networks (RNN) [26].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Sea Surface Chlorophyll-a Concentrations Based on Deep Learning and Time-Series Remote Sensing Data

Yao,

Wang,

Zhang

et al. 2023

Remote Sensing

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Accurate prediction of future chlorophyll-a (Chl-a) concentrations is of great importance for effective management and early warning of marine ecological systems. However, previous studies primarily focused on chlorophyll-a inversion and reconstruction, while methods for predicting Chl-a concentrations remain limited. To address this issue, we adopted four deep learning approaches, including Convolutional LSTM Network (ConvLSTM), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Eidetic 3D LSTM (E3D-LSTM), and Self-Attention ConvLSTM (SA-ConvLSTM) models, to predict Chl-a over the Yellow Sea and Bohai Sea (YBS) in China. Furthermore, 14 environmental variables obtained from the remote sensing data of Moderate-resolution Imaging Spectroradiometer (MODIS) and ECMWF Reanalysis v5 (ERA5) were utilized to predict the Chl-a concentrations in the study area. The results showed that all four models performed satisfactorily in predicting Chl-a concentrations in the YBS, with SA-ConvLSTM exhibiting a closer approximation to true values. Furthermore, we analyzed the impact of the Self-Attention Memory Module (SAM) on the prediction results. Compared to the ConvLSTM model, the SA-ConvLSTM model integrated with the SAM module better captured subtle large-scale variations within the study area. The SA-ConvLSTM model exhibited the highest prediction accuracy, and the one-month Pearson correlation coefficient reached 0.887. Our study provides an available approach for anticipating Chl-a concentrations over a large area of sea.

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Long-term prediction of sea surface chlorophyll-a concentration based on the combination of spatio-temporal features

Cited by 25 publications

References 60 publications

An Intelligent Early Warning System for Harmful Algal Blooms: Harnessing the Power of Big Data and Deep Learning

An Intelligent Early Warning System for Harmful Algal Blooms: Harnessing the Power of Big Data and Deep Learning

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

Prediction of Sea Surface Chlorophyll-a Concentrations Based on Deep Learning and Time-Series Remote Sensing Data

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