Estimation of Nonpoint Source Pollutant Loads of Juam-Dam Basin Based on the Classification of Satellite Imagery

Lee, Geun-Sang; Kim, Tae-Keun

doi:10.11108/kagis.2012.15.3.001

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Currently, it provides both domestic and industrial water to cities including Gwangju, Suncheon, Gwangyang, and Yeosu [26]. The catchment area of Juam Lake is 1010 km 2 , and its storage capacity is 457 million m 3 [27,28]. The study area with the locations of both automatic and manual monitoring stations is presented in Figure 1.…”

Section: Monitoring Sites Of Juam Lakementioning

confidence: 99%

Determination of Optimal Water Intake Layer Using Deep Learning-Based Water Quality Monitoring and Prediction

Kim,

Kwak,

Lee

et al. 2023

Water

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The effective management of drinking water sources is essential not only for maintaining their water quality but also for the efficient operation of drinking water treatment plants. A decline in water quality in water reservoirs can result in increased operational costs for water treatment and compromise the reliability and safety of treated water. In this study, a deep learning model, the long short-term memory (LSTM) algorithm, was employed to predict water quality and identify an optimal water intake layer across various seasons and years for Juam Lake, Korea. A comprehensive investigation was conducted to prioritize various water quality parameters and determine suitable intake layers. Based on these priorities, effective methods for optimizing an intake layer were developed to enable more reliable water intake operations. Water quality data from January 2013 to June 2023 were analyzed for the study. This dataset was used for rigorous statistical and correlational analyses to better understand the dynamics affecting water quality parameters. The findings aim to enhance the operational efficiency of water intake and treatment facilities.

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Section: Monitoring Sites Of Juam Lakementioning

confidence: 99%