Juhwan Moon
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Juhwan Moon has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Hong-Sik Yun
Geodesy Laboratory, Civil & Architectural and Environmental System Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea
1 shared publication - Jae-Joon Lee
Department of Fire Safety Engineering, Jeonju University, Jeonju, Jeollabuk-do, Republic of Korea
1 shared publication - Sang-Won Lee
Interdisciplinary Program in Crisis, Disaster and Risk Management, Sungkyunkwan University, Seoul 03063, Korea
1 shared publication
Latest Publications
- Open Access
Urban River Dissolved Oxygen Prediction Model Using Machine Learning
This study outlines the preliminary stages of the development of an algorithm to predict the optimal WQ of the Hwanggujicheon Stream. In the first stages, we used the AdaBoost algorithm model to predict the state of WQ, using data from the open artificial intelligence (AI) hub. The AdaBoost algorithm has excellent predictive performance and model suitability and was selected for random forest and gradient boosting (GB)-based boosting models. To predict the optimized WQ, we selected pH, SS, water temperature, total nitrogen(TN), dissolved total phosphorus(DTP), NH3-N, chemical oxygen demand (COD), dissolved total nitrogen (DTN), and NO3-N as the input variables of the AdaBoost model. Dissolved oxygen (DO) was used as the target variable. Third, an algorithm showing excellent predictive power was selected by analyzing the prediction accuracy according to the input variable by using the random forest or GB series algorithm in the initial model. Finally, the performance evaluation of the ultimately developed predictive model demonstrated that RMS was 0.015, MAE was 0.009, and R2 was 0.912. The coefficient of the variation of the root mean square error (CVRMSE) was 17.404. R2 0.912 and CVRMSE were 17.404, indicating that the predictive model developed meets the criteria of ASHRAE Guideline 14. It is imperative that government and administrative agencies have access to effective tools to assess WQ and pollution levels in their local bodies of water.
Open AccessUrban River Dissolved Oxygen Prediction Model Using Machine Learning
This study outlines the preliminary stages of the development of an algorithm to predict the optimal WQ of the Hwanggujicheon Stream. In the first stages, we used the AdaBoost algorithm model to predict the state of WQ, using data from the open artificial intelligence (AI) hub. The AdaBoost algorithm has excellent predictive performance and model suitability and was selected for random forest and gradient boosting (GB)-based boosting models. To predict the optimized WQ, we selected pH, SS, water temperature, total nitrogen(TN), dissolved total phosphorus(DTP), NH3-N, chemical oxygen demand (COD), dissolved total nitrogen (DTN), and NO3-N as the input variables of the AdaBoost model. Dissolved oxygen (DO) was used as the target variable. Third, an algorithm showing excellent predictive power was selected by analyzing the prediction accuracy according to the input variable by using the random forest or GB series algorithm in the initial model. Finally, the performance evaluation of the ultimately developed predictive model demonstrated that RMS was 0.015, MAE was 0.009, and R2 was 0.912. The coefficient of the variation of the root mean square error (CVRMSE) was 17.404. R2 0.912 and CVRMSE were 17.404, indicating that the predictive model developed meets the criteria of ASHRAE Guideline 14. It is imperative that government and administrative agencies have access to effective tools to assess WQ and pollution levels in their local bodies of water.