인공지능 딥러닝을 활용한 조류현상 예측기술 개발 및 활용방안 = Development and Application of Algal Bloom Using Artificial Intelligence Deep Learning

다국어 초록 (Multilingual Abstract)

Ⅰ. Background and Aims of Research
1. Research outline
□ Research title: Development and application of an algal bloom forecast system using artificial intelligence deep learning technology
□ Research period: January 1, 2020 ~ December 31, 2020
2. Necessity and purpose of research
□ Limitations of the current algal bloom warning system
ㅇ The Ministry of Environment and the National Institute of Environmental Research implemented an algal bloom warning system based on the measured values of harmful blue-green algae and the EFDC model.
ㅇ Limitations of physics-based models
- They have a solid theoretical background but there is a difficulty in securing the detailed data required by the model.
- Since algal blooms are living organisms, the law of conservation of mass does not apply to the number of harmful blue-green algae cells. Therefore, the physics-based model has limitations.
- Deep learning-based forecasting can be considered as an alternative and a complementary method.
Ⅱ. Current Algal Bloom Response Policy
1. Algal bloom warning system
□ Year of introduction: 1998
□ Legal basis: Article 21 of the Water Environment Conservation Act
□ Target
ㅇ 28 branches of water supply sources and hydrophilic activities
ㅇ Issuer: Basin Environmental Office and local governments
□ Analysis items
ㅇ Measured numbers of harmful blue-green algae cells
ㅇ Based on water source section
- Attention: 1,000 (cells/mL) or more
- Alert: 10,000 (cells/mL) or more
- Large bloom: 1,000,000 (cells/mL) or more
ㅇ Based on hydrophilic activities section
- Attention: 20,000 (cells/mL) or more
- Alert: 100,000 (cells/mL) or more
2. (Former) Water quality forecast system
□ Year of Introduction: 2012
□ Legal basis: Article 21 of the Water Environment Conservation Act
□ Target
ㅇ 17 branches including 16 barrages and the Bukhan River Sambong-ri of the four major rivers of South Korea
ㅇ Issuer: National Institute of Environmental Research
□ Analysis items
ㅇ Predicted water temperature and chlorophyll-a concentration
ㅇ Currently, as the algal bloom warning system and the water quality forecast system are integrated, no forecast is issued although forecasting is performed.
□ Providing forecasts for harmful blue-green algae cells
ㅇ Twice a week, Monday and Thursday, six branches that are targets of the algal bloom system
ㅇ Issuing the predicted number of harmful blue-green algae cells and water temperature predictions
3. Status of the water quality monitoring network
□ Legal basis
ㅇ Article 22 of the Basic Act on Environmental Policy and Article 9 of the Water Environment Conservation Act
□ Organization
ㅇ Water quality monitoring network
- Target: water quality measurement data in rivers, lakes, agricultural water, urban streams, and industrial rivers
- Provided information: water depth, hydrogen ion concentration, dissolved oxygen content, BOD, COD, suspended matter, total nitrogen, total phosphorus, total organic carbon (TOC), water temperature, phenols, electrical conductivity, total coliform group, dissolved total nitrogen, ammonia nitrogen, nitrate nitrogen, dissolved total phosphorus, phosphate phosphorus, chlorophyll a, transparency
- Cycle: once a month, once a week for major locations
ㅇ Total quantity measurement network
- Target: basic data for total amount management in areas subject to the total water pollution rate system
- Provided information: water temperature, hydrogen ion concentration, electrical conductivity, dissolved oxygen, BOD, COD, suspended matter, total nitrogen, total phosphorus, TOC, flow rate
- Cycle: once a month
ㅇ Automatic measurement network
- Operated to complement the hand-operated measurements of the water quality monitoring network
- Provided information:
(Common) water temperature, hydrogen ion concentration, dissolved oxygen content, electrical conductivity, TOC
(Optional) Turbidity, chlorophyll a, TN, TP, NH₃-N, NO₃-N, PO₃-P, VOCs (nine types, ten items), phenol, heavy metals, biological monitoring items
- Cycle: once a day
ㅇ Sediment monitoring network
- Purpose: investigation of the physicochemical properties of sediments in public waters subject to water quality conservation of South Korea
- Provided information:
(Common) water temperature, hydrogen ion concentration, dissolved oxygen content, electrical conductivity, TOC
(Optional) maximum depth during collection, surface measurement depth, surface and bottom depth, water temperature, dissolved oxygen content, pH, electrical conductivity, sediment particle size, moisture content, ratio and grade of complete combustion potential, COD, TOC, TN, TN grade, TP, SRP, heavy metals, conservative element concentration
- Cycle: (River) twice a year for the first and second halves, (Lake) once a year
ㅇ In addition, there are additional measurements of radioactive monitoring networks and biometric networks.
Ⅲ. Water Quality Prediction Models
1. Physics-based model
□ Example
ㅇ EFDC, QUAL2K, WASP, etc.
ㅇ The National Institute of Environmental Research is operating an EFDC-based model.
□ Organization
ㅇ Construct a grid network by dividing the water system into sub-regions and set boundary conditions
ㅇ Estimate the water quality in sub-area units within the grid
2. Deep learning algorithm
□ Model structure
ㅇ Multi-layer perceptron (MLP)
- It mimics the neurons and synapses of a neural network. It consists of an input layer, a hidden layer, and an output layer. it has a multi-layered structure with more than one hidden layer.
ㅇ Recurrent Neural Network (RNN)
- It additionally reflects the feedback effects of previous hidden nodes.
- Nowadays, GRU and LSTM models are used. These models utilize the long-term memory based on a simple recurrent neural network.
3. Physics-based model vs. Deep learning algorithm
□ Physics-based model
ㅇ Based on well-established mathematical/physical laws
ㅇ Actual observations are used for model evaluation.
ㅇ Prediction can be performed at a more detailed resolution than observed values based on physical equations.
ㅇ Disadvantages
- Errors due to uncertain initial/boundary conditions
- Difficulty in predicting the abnormal phenomena
- May not work due to problems such as poor input data, instability of model relations, modeling method, etc.
□ Deep learning algorithm
ㅇ Establish the relationship between input and output variables through machine learning
ㅇ Actual observations are used for model construction.
ㅇ Includes error conditions in the model by quantifying the error of the measurements
ㅇ Advantages in short-term predictions with greater uncertainties compared to physics-based models
ㅇ Disadvantages
- Requires a huge amount of data
- Cannot be performed at a more detailed resolution than observation resolution
- Practical application is limited since the relationship between input and output variables cannot be explained.
Ⅳ. Development of an Algal Bloom Forecast Algorithm Based on Deep Learning
1. Data collection and preprocessing
□ Model construction target
ㅇ Target point: algae observation point in the hydrophilic activity section of the Han River
ㅇ Target variable
- Direct prediction of the number of harmful blue-green algae cells which is the direct cause of the algal bloom
- Differentiated from previous studies that indirectly predicted the algal bloom through chlorophyll a prediction
□ Model construction period
ㅇ Target period: April 2007 ~ August 2020
ㅇ Data in winter from December to March, which is relatively safe from algal blooms, are excluded.
2. Characteristics of algae data
□ Descriptive statistics
□ Characteristics
ㅇ Extremely right-skewed asymmetric distribution
ㅇ Extreme asymmetric distribution is exhibited since algal blooms occur intensively in summer when the temperature is high.
ㅇ Because of this, it is difficult to directly predict harmful blue-green algae using physics-based models or traditional statistical models.
3. Development of a predicting algorithm
□ RNN model construction
ㅇ Target of prediction: the number of harmful blue-green algae cells
ㅇ Constructing an LSTM prediction algorithm to utilize the long-term memory information
ㅇ Loss function for optimization: least squares function Optimization algorithm: ADAM
ㅇ Training data: April 2007 ~ November 2016 Test data: April 2017 ~ June 2020
□ Results
ㅇ The increasing and decreasing patterns are well predicted although there is difficulty in predicting using traditional prediction methods due to high data instability, which results from the fact that the hydrophilic activity section is located downstream of the river.
ㅇ Well predict the occurrence of the largest extreme value at the same time
ㅇ Prediction error
Ⅴ. Conclusion and Achievements
□ Achievements
ㅇ Since the prediction using a physical model is established based on a well-established theory, it is widely used to predict properties of water quality such as water temperature, dissolved oxygen, total phosphorus, and total nitrogen. The prediction using the physical equation based on the law of conservation of mass is well suited for conservative substance. However, there is a limitation in the prediction of algae cells since it is the activity of living organisms.
ㅇ Existing algal phenomena prediction studies have not directly predicted the number of harmful blue-green algae cells, which is the direct cause of algal phenomena. It is replaced by using the results of chlorophyll a concentration prediction.
ㅇ In this study, a deep learning algorithm based on recurrent neural networks was used as an alternative method to predict the number of harmful blue-green algae cells. It well predicted the increasing or decreasing patterns of algae and the occurrence of abnormal phenomena at the concurrent point.
□ Limitations
ㅇ Only water quality, upstream water quality, water level, and meteorological information were used as input variables. These variables are already used in the physical model. Taking into account social variables such as population change and the benefits of deep learning analytics can be leveraged to a greater extent. Unstructured information such as satellite images can be additionally considered.
ㅇ There is a limitation in the amount of data. In this study, the model was studied using data from a total of 365 weekly data collections from 2007 to 2016, but this amount itself is not sufficient. Whenever new data are added, the predictive model should be updated to increase the prediction efficiency.
ㅇ There is a limitation due to the black-box characteristic. The detailed operational process of the prediction model cannot be clearly observed. When implementing a policy, evidence is needed. The black-box characteristic of deep learning prediction models makes it difficult to provide clear evidence.
□ Conclusions and suggestions
ㅇ Because it is very simple to perform predictions with the model that has already been established, it can be directly used as reference information for current algal bloom forecasts.
ㅇ Since predictions using deep learning models and physics-based models both have advantages and disadvantages, it is most desirable to integrate the two prediction methods. Based on the deep learning model, the physical model can be integrated by including the physical equation in the constraint of the objective function. Or, deep learning can be partially performed in the partial module of the physical model prediction.