A Study on Characteristics of Water Quality using Multivariate Analysis in Sumjin River Basin

Park, Jin-Hwan; Moon, Myungjin; Lee, Hyungjin; Kim, Kapsoon

doi:10.15681/kswe.2014.30.2.119

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…Therefore, it is widely used in decision-making in various fields of research [19][20][21]. The AHP has also been used in civil engineering research to determine the age of water pipes [22] and prioritize river water quality improvement [23]. It has also been used in disaster prediction to analyze landslide risk areas [24].…”

Section: Ahp Analysismentioning

confidence: 99%

A Study on Factors Influencing Ground Subsidence and a Risk Analysis Method Using the Attributes of Sewer Pipes

Lee,

Kang,

Kim

2023

Applied Sciences

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In recent years, we have witnessed an increase in road subsidence accidents in urban areas, threatening the safety of citizens. Various road facilities, such as water and sewage pipes, and telecommunication facilities are buried under roads, and the aging of these facilities is one of the factors causing road subsidence. In particular, old sewer pipes are a primary cause of road subsidence. However, most maintenance work on such facilities is carried out based on how long ago they were buried underground, without considering the risk of road subsidence caused by them. Therefore, this study aims to present a reliable method to assess road subsidence risk that considers various sewer pipe specifications and the environment surrounding them. To derive the factors influencing subsidence, sewer pipes near the target region, where road subsidence occurs the most, were extracted to analyze the correlation between road subsidence, pipe integrity, and the surrounding environment. An effective analysis method was selected by comparing logistic regression analysis and AHP (Analytic Hierarchy Process) analysis, and a weighted road subsidence risk assessment method was proposed by evaluating the importance of factors affecting ground subsidence. Its applicability was examined by comparing actual road subsidence data and analyzing risk in a pilot study area to validate the reliability of the proposed methodology. The results showed that it was possible to make reliable predictions of road subsidence risk areas.

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Section: Ahp Analysismentioning

confidence: 99%

A Study on Factors Influencing Ground Subsidence and a Risk Analysis Method Using the Attributes of Sewer Pipes

Lee,

Kang,

Kim

2023

Applied Sciences

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“…Principal component analysis is a method of summarizing and analyzing a small number of comprehensive characteristics using linear combinations of independent variables while minimizing the loss of information. It is a technique that is often used to identify factors behind a particular idea or when it is more reasonable to deal with specific ideas and their background factors comprehensively rather than independently (Gwak and Kim, 2016 [15]; Park and Rhee, 2012 [16]). For example, it would be more reasonable and rational to identify measured items used for safety monitoring of dams by integrating them into several common factors rather than to identify them in detail by instrument.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Behavior of Porewater Pressures in an Earth Dam by Principal Component Analysis

Yun

Kim²,

Im³

et al. 2022

Water

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This study deals with the utilization of the pore pressure meter for evaluating the stability of a dam through the correlation between the porewater pressure installed in the fill dam and the water level of the dam. To this end, principal components analysis was performed on a total of 18 porewater pressure meters, and the main components were classified into three groups: internal (Group A), external (Group B), and upper (Group C), on the basis of the seepage line formed within the dam body. The coefficient of correlation between the porewater pressure and water level was found to be 0.86 to 1.00, indicating a strong positive linear relationship. This means that the maintenance of the dam is possible through the pore pressure meter present in Group A. Furthermore, the regression analysis for porewater pressures and water levels resulted in a linear regression model with the coefficient of determination (R2) of Group A being between 0.74 and 0.99. In particular, R2 between the power water pressure installed at the base of the dam and the water level was more than 0.99. Therefore, it was shown that the prediction of the porewater pressure is possible by using the relationships with the water level, making it possible to determine the safety of the dam by comparing it with the currently measured values.

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“…Changes in river WQ have been predicted through traditional time series modeling for various forms of water pollution [10,37], and the amount of research being conducted is on the rise [23,38], as the size of the data has grown and the limitations of the traditional time series model have been revealed. As an alternative to this, a deep learning-based or machine-learning-based prediction model is emerging [12].…”

Section: Prior Researchmentioning

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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A Study on Characteristics of Water Quality using Multivariate Analysis in Sumjin River Basin

Cited by 12 publications

References 9 publications

A Study on Factors Influencing Ground Subsidence and a Risk Analysis Method Using the Attributes of Sewer Pipes

A Study on Factors Influencing Ground Subsidence and a Risk Analysis Method Using the Attributes of Sewer Pipes

Behavior of Porewater Pressures in an Earth Dam by Principal Component Analysis

Urban River Dissolved Oxygen Prediction Model Using Machine Learning

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