Evaluation of Watershed Water Quality Management According to Flow Conditions through Factor Analysis and Naïve Bayes Classifier
-
Published:2023-06-25
Issue:13
Volume:15
Page:10038
-
ISSN:2071-1050
-
Container-title:Sustainability
-
language:en
-
Short-container-title:Sustainability
Author:
Jung Woo Suk1,
Kim Young Do2ORCID
Affiliation:
1. Nakdong River Support Team, Presidential Water Commission, Changwon-si 51439, Republic of Korea
2. Department of Civil & Environmental Engineering, Myongji University, Yongin 17058, Republic of Korea
Abstract
Previous studies on water quality assessment for watershed management have predominantly focused on specific seasonal or annual average values, rather than considering water quality variations based on flow fluctuations. It is crucial to identify the water quality characteristics within a watershed by incorporating flow conditions to establish a customized watershed management approach over different time periods. In this study, a vulnerability analysis was conducted to attain the target water quality (TWQ) in 22 watersheds within the Nakdong River system in South Korea. Additionally, factor analysis (FA) was employed to analyze the characteristics of water quality fluctuations in relation to flow conditions. The FA results categorized the pollution source characteristics of the 22 watersheds into various types, indicating the need for specific pollution source management strategies. These findings enabled an initial decision-making process regarding which water pollution sources to prioritize based on flow conditions. Moreover, detailed analyses of pollution sources were performed for watersheds, where achieving TWQ was challenging. Subsequently, a data-based prediction model was developed using the naïve Bayes classification model to determine the likelihood of achieving TWQ. As a result, this study proposes a technique for water quality management in watersheds by introducing a water quality excess probability model, which employs data-based analysis instead of traditional numerical modeling for watershed water quality assessment and proactive prediction. The study discusses the potential of various data-based tools to reduce development and analysis time, providing a powerful alternative to physical-based models that require extensive input data and are time-consuming. To advance future studies, the establishment of comprehensive water environment big data, improvement of real-time monitoring systems within watersheds, and advancements in spatial and temporal observation technologies are emphasized as essential for the development of an advanced watershed management system.
Funder
Korea Environmental Industry & Technology Institute
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference34 articles.
1. Research on How to Set 3rd Phase Target Water Quality on the Boundary between Metropolitan Cities/Dos Specified in Nakdong River Basin;Hwang;J. Korean Soc. Water Environ.,2017
2. Calibration and validation of watershed models and advances in uncertainty analysis in TMDL studies;Ahmadisharaf;J. Hydrol. Eng.,2019
3. A Study on the Variation of Water Quality and the Evaluation of Target Water Quality Using LDC in Major Tributaries of Nakdong River Basin;Lee;J. Korean Soc. Water Environ.,2020
4. A Study on the Spatial Variation of Target Water Quality and Excess Rate at 41 Stations in Nakdong River Basin after the Total Maximum Daily Loads;Cho;J. Korean Soc. Agric. Eng.,2020
5. Assessment of Total Maximum Daily Loads (TMDLs) for water quality management in the Nakdong River basin, South Korea;Kim;Environ. Monit. Assess.,2018