Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods

Shin, Yuna; Kim, Taekgeun; Hong, Seoksu; Lee, Seulbi; Lee, Eunji; Hong, Suk Bong; Lee, ChangSik; Kim, TaeYeon; Park, Man Sik; Park, Jungsu; Heo, Tae‐Young

doi:10.3390/w12061822

Cited by 70 publications

(37 citation statements)

References 43 publications

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“…Marine research using deep learning technology can be divided into prediction-related research, classification-related research, and research on methods to correct missing values. Prediction-related research has been applied to various topics, such as the El Niño Index, chlorophyll-a time series, and sea surface temperature [9][10][11]. Classification-related research has been conducted to classify marine life using image data.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration

et al. 2021

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In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R2) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration

et al. 2021

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“…In recent years, the application of various machine learning techniques and methods has been explored for developing water quality parameters prediction models [3][4][5][6]. As a result, these models have become topical research hotspots in the field of aquaculture and water engineering.…”

Section: Introductionmentioning

confidence: 99%

Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm

Eze

Halse

Ajmal

2021

Water

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Providing an accurate prediction of water quality parameters for improved water quality management is a topical issue in the aquaculture industry. Conventional prediction methods have shown different challenges like a poor generalization, poor prediction accuracy, and high time complexity. Aiming at these challenges, a novel hybrid prediction model with ensemble empirical mode decomposition (EEMD) and deep learning (DL) long-short term memory (LSTM) neural network is proposed in this paper. In this innovative hybrid EEMD-DL-LSTM model, firstly, the integrity of the datasets is enhanced by applying moving average filtering and linear interpolation techniques of water quality parameter datasets pre-treatment. Secondly, the measured real sensor water quality parameters dataset is decomposed with the aid of the EEMD algorithm into disparate IMFs and a corresponding residual item. Thirdly, a multi-feature selection process is applied to make a careful selection of a strongly correlated group of IMFs with the measured real water quality parameter datasets and integrate them as inputs to the DL-LSTM neural network. The presented model is built on water quality sensor data collected from an Abalone farm in South Africa. The performance of the novel hybrid prediction model is validated by comparing the results against the real datasets. To measure the overall accuracy of the novel hybrid prediction model, different statistical indices, namely the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), are used.

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“…Several studies have been conducted to establish a means of coping with water quality impairments caused by algal biomass using conventional numerical modelling methods, least squares support vector regression (LSSVR), neural networks methods such as Radial Basis Function neural network (RBFNN), Back Propagation neural network (BPNN) algorithms, and machine learning methods to predict chlorophyll-a concentrations as an indicator for future water quality changes [9][10][11][12]. However, the challenge with traditional numerical methods, LSSVR, and neural networks such as RBFNN and BPNN is the inherent weakness of the long-term dependency problem.…”

Section: Introductionmentioning

confidence: 99%

Time Series Chlorophyll-A Concentration Data Analysis: A Novel Forecasting Model for Aquaculture Industry

Eze

Kirby

Attridge

et al. 2021

The 7th International Conference on Time Series and Forecasting

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Eutrophication in fresh water has become a critical challenge worldwide and chlorophyll-a content is a key water quality parameter that indicates the extent of eutrophication and algae concentration in a body of water. In this paper, a forecasting model for the high accuracy prediction of chlorophyll-a content is proposed to enable aquafarm managers to take remediation actions against the occurrence of toxic algal blooms in the aquaculture industry. The proposed model combines the ensemble empirical mode decomposition (EEMD) technique and a deep learning (DL) long short-term memory (LSTM) neural network (NN). With this hybrid approach, the time-series data are firstly decomposed with the aid of the EEMD algorithm into manifold intrinsic mode functions (IMFs). Secondly, a multi-attribute selection process is employed to select the group of IMFs with strong correlations with the measured real chlorophyll-a dataset and integrate them as inputs for the DL LSTM NN. The model is built on water quality sensor data collected from the Loch Duart salmon aquafarm in Scotland. The performance of the proposed novel hybrid predictive model is validated by comparing the results against the dataset. To measure the overall accuracy of the proposed novel hybrid predictive model, the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) were used.

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Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods

Cited by 70 publications

References 43 publications

A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration

A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration

Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm

Time Series Chlorophyll-A Concentration Data Analysis: A Novel Forecasting Model for Aquaculture Industry

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