Harmful algal blooms (HABs) can cause serious problems for aquatic ecosystems and human health, as well as massive social costs. Therefore, continuous monitoring and prevention are required. Water quality management is an important task to minimize such algae, and future occurrences can be accurately predicted through optimal water resource management. In this study, we developed a convolutional neural network model using eight water quality variables and four weather variables to predict the concentration of chlorophyll-a in four major Korean rivers. In addition, Deep SHAP was applied to aid in policy decision-making and identify the influence on variables affecting chlorophyll-a. This integrated prediction model showed a 38.01 % reduction in root mean square error and 36.16 % improvement in R-squared compared to the long short-term memory (LSTM) model. This demonstrated the effectiveness of the proposed integrated prediction approach. Furthermore, despite simultaneously predicting HABs at all monitoring stations and training 394 times faster than LSTM-based models, the proposed method exhibited a significant improvement in efficiency and elucidated variable influences that existing models failed to explain. The proposed integrated prediction model can predict HAB spread, identify variable influences to aid decision-makers, and effectively implement preemptive responses, thus reducing economic losses and preserving aquatic ecosystems.

Harmful algal bloom (HAB) can lead to severe problems in aquatic ecosystems and human health. Therefore, it requires constant monitoring and prevention. Water quality management is one of the most important tasks to minimize this phenomenon, and its future occurrence can be accurately predicted through optimal water resource management. This study developed a convolutional neural network model that can predict chlorophyll-a in the Four Major Rivers of South Korea through one deep learning model, using multiple parameters that consider eight types of water quality data and four types of weather data. This integrated prediction model showed a reduction of 38.01% in root mean square error (RMSE) and an improvement of 36.16% in R-squared compared to the long short-term memory (LSTM) model used in previous predictions of chlorophyll-a. This demonstrates the effectiveness of integrated prediction. Furthermore, by performing training 394 times faster than the LSTM-based model despite simultaneously predicting the HAB of all monitoring stations, the integrated prediction model showed a greatly improved efficiency. Through the integrated prediction model presented in this study, the proliferation of HAB can be mitigated, and pre-emptive measures can be more effectively implemented, thereby contributing to reducing economic losses and preserving aquatic ecosystems.

In this paper, we propose a real-time prediction model that can respond to particulate matters (PM) in the air, which are an indication of poor air quality. The model applies interpolation to air quality and weather data and then uses a Convolutional Neural Network (CNN) to predict PM concentrations. The interpolation transforms the irregular spatial data into an equally spaced grid, which the model requires. This combination creates the interpolated CNN (ICNN) model that we use to predict PM10 and PM2.5 concentrations. The PM10 and PM2.5 evaluation results show an effective prediction performance with an R-squared higher than 0.97 and a root mean square error (RMSE) of approximately 16% of the standard deviation. Furthermore, both PM10 and PM2.5 prediction models forecast high concentrations with high reliability, with a probability of detection higher than 0.90 and a critical success index exceeding 0.85. The proposed ICNN prediction model achieves a high prediction performance using spatio-temporal information and presents a new direction in the prediction field.

Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study’s models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society.