Recovery of Fish Community and Water Quality in Streams Where Fish Kills have Occurred

Lee, Jae Yong; Lee, Kwang-yeol; Lee, Saeromi; Choi, Jaeseok; Lee, Seok Jong; Jung, Susanne; Jung, Myoung-Sook; Kim, Bomchul

doi:10.11614/ksl.2013.46.2.154

Cited by 5 publications

(2 citation statements)

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“…Despite the river maintenance project, the WQ of downtown rivers is deteriorating. Dissolved oxygen (DO) is among the WQ elements of downtown rivers that are worsening due to water pollution [4], and as a result, various WQ accidents occur frequently. According to the Seoul Institute of Health and Environment (2018), over the past 13 years (2005-2017), there have been about 50 WQ accidents in Seoul.…”

Section: Introductionmentioning

confidence: 99%

Urban River Dissolved Oxygen Prediction Model Using Machine Learning

Moon

Lee

et al. 2022

Water

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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.

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Section: Introductionmentioning

confidence: 99%

Urban River Dissolved Oxygen Prediction Model Using Machine Learning

Moon

Lee

et al. 2022

Water

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“…Most importantly, sharp drops in dissolved oxygen (DO) concentrations, an important water quality parameter that indicates the suitability of water for supporting aquatic life, are noticeable in urban streams. These low‐DO levels are of particular concern for the managing authorities of water resources as they are a major cause of fish kills in urban streams (Huang et al., 2017; Paerl et al., 1998; Thronson & Quigg, 2008) including streams in Seoul, the capital of South Korea (Lee et al., 2013). Therefore, to address these concerns, it is necessary to develop forecasting models that can predict the depletion of DO in urban streams.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Abrupt Depletion of Dissolved Oxygen in Urban Streams Using Discontinuously Measured Hourly Time‐Series Data

Kim

Shin

et al. 2021

Water Resources Research

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Rivers provide the most accessible water resources for a variety of uses such as drinking water, wildlife conservation, and recreation (Bao et al., 2018;Hietel et al., 2004;Riechel et al., 2016). However, as urbanization continues worldwide, the growth and concentration of populations, and the associated increase in pollution sources, land use changes, and hydromorphological modifications have made the water quality of urban streams more susceptible to deterioration (Everard & Moggridge, 2012;He et al., 2008;Yoshimura et al., 2005). Most importantly, sharp drops in dissolved oxygen (DO) concentrations, an important water quality parameter that indicates the suitability of water for supporting aquatic life, are noticeable in urban streams. These low-DO levels are of particular concern for the managing authorities of water resources as they are a major cause of fish kills in urban streams (

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