Evaluation of Watershed Water Quality Management According to Flow Conditions through Factor Analysis and Naïve Bayes Classifier

Woo Suk Jung and Young Do Kim ()
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Woo Suk Jung: Nakdong River Support Team, Presidential Water Commission, Changwon-si 51439, Republic of Korea
Young Do Kim: Department of Civil & Environmental Engineering, Myongji University, Yongin 17058, Republic of Korea

Sustainability, 2023, vol. 15, issue 13, 1-17

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.

Keywords: TMDL; target water quality (TWQ); watershed management; water quality assessment; factor analysis; naïve Bayes classifier (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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