Jin Hyo Lee

In this study, a customized WQI (Seoul water quality index, S-WQI) for urban rivers that can ultimately reflect their characteristics was developed by modifying and supplementing the existing Bascarόn WQI calculation method through linkage with statistical methods such as factor analysis. We used the water quality data generated monthly at 17 water quality monitoring networks (WQMNs) in Seoul for 18 years, from 2002 to 2019. Results of the research show that the monthly S-WQI showed an average 70 out of 100, ‘good (II)’ grades, whereas the average water quality grade according to the environmental standards was ‘slightly good (II),’ with an R2 value of 0.8298. The annual S-WQI was found to be 39 (bad) to 97 (very good), with an average of 72 (good). Through this study, S-WQI, a customized WQI for urban rivers, was judged to be a reasonable index that can represent the characteristics of urban river water quality. This is because it is easy to apply and is a calculation method that uses relatively fewer water quality items than the WQI calculated in the past, and it is highly likely to be linked to the currently implemented water quality grade system. In addition, to extend the application of WQI to various water quality survey points, based on the calculation methodology performed to derive the indices in this study, such as modified S-WQI (MS-WQI), by adding new water quality items and changing some items, it is also possible to develop an advanced customized WQI for urban rivers considering watershed characteristics and measurement items.

We developed a classification model and a real-time prediction model for short-term dissolved oxygen (DO) at the junction of the Han River in Anyangcheon, where water quality accidents occur frequently. The classification model is an analysis model that derives the main factors affecting DO changes in the Anyangcheon mobile water quality monitoring network using decision tree, random forest, and XGBoost. The model identified the key factors affecting DO changes to be electrical conductivity, cumulative precipitation, total nitrogen, and water temperature. Random forest (sensitivity, 0.9962; accuracy, 0.9981) and XGBoost (sensitivity, 1.0000; accuracy, 0.9822) showed excellent classification performance. The real-time prediction model for short-term DO that we developed adopted artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU) algorithms. LSTM (R2 = 0.93 − 0.97, first half; R2 = 0.95 − 0.96, second half) and GRU (R2 = 0.94 − 0.98, first half; R2 = 0.96 − 0.98, second half) significantly outperformed ANN (R2 = 0.64 − 0.86). The LSTM and GRU models we developed used real-time automatic measurement data, targeting urban rivers that are sensitive to water quality changes and are waterfront areas for citizens. They can quickly reflect and simulate short-term, real-time changes in water quality compared with existing static models.

Depletion of dissolved oxygen (DO) is a major cause of fish kills in urban streams. Although forecasting short‐term DO concentrations in streams prior to hypoxic events is necessary, such efforts have been rarely made. In this study, 24‐h forecasting models were developed for DO concentrations in three urban streams of South Korea. To forecast the DO concentrations at the outlet sites, which coincide with fish kill hot spot areas, water quality parameters at the lower reaches and hydrometeorological parameters were used as input variables. The monitoring data were measured hourly between 2017 and 2018 and divided into training and test sets at a ratio of 8:2. Tenfold cross validation was performed for hyperparameter optimization. Due to the dynamic characteristics of DO concentrations and the discontinuity in time‐series data, a long short‐term memory (LSTM) neural network modeling approach was selected. Overall, a high degree of accuracy was recorded for all study streams. Although hypoxic events were forecast with lower accuracy, the timing and magnitude of abrupt DO depletion were well captured. Water temperature and DO concentrations at the lower reaches and 24‐h cumulative precipitation were important variables for forecasting DO concentrations at all stream outlets. In particular, the importance of cumulative precipitation across all streams indicated that the effects of nonpoint sources were critical in depleting DO in urban streams. Monitoring of both lower reaches and outlets in conjunction with a variable importance analysis enhanced interpretability of the LSTM model outputs. This study improves our understanding of precursors of hypoxic events in urban streams.