• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.71-86
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• 2022

The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DO_Bot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DIN_Sur) and dissolved inorganic phosphorus (DIP_Sur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.

• Journal of Korean Society on Water Environment
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• v.26 no.5
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• pp.796-801
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• 2010

Water quality index (WQI) can be a great tool that allows experts to translate large amount of complex water quality data into a format more easily understood by the public and policy makers. Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) can be calculated with the three factors (Scope: $F_1$, Frequency: $F_2$, Amplitude: $F_3$). After all, the WQI for a specific site is produced as a number between 0 to 100; the scale is also divided into five categories, i.e., Excellent, Good, Fair, Marginal and Poor. The WQI was found to be highly related to Chl-a, pH, temperature among the collected items. When the more input parameters were used, the range of variation generally became smaller. $F_3$ among the factors of WQI was influenced by algae. It showed a similar variation tendency between WQI and algal bloom in 2008.

• Korean Journal of Ecology and Environment
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• v.46 no.2
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• pp.225-233
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• 2013

We examined the effect of different field temperatures on water quality improvement (WQI) of natural domestic plant-mineral composites (PMCs). This method was previously used by Kim et al. (2011), to monitor the restoration of water quality of a eutrophic lake, Lake Shingal (Korea). Results indicate that PMCs on phytoplankton, BOD and phosphorus showed more than 70% WQI below $20^{\circ}C$, and less than 40% WQI over $25^{\circ}C$, respectively. The WQIs of PMCs on blue-green algae were gradually decreased with the increase of temperature, whilst diatoms exhibited more than 90% higher WQIs, regardless of water temperature. Additionally, the WQIs on bacterial biomass and total nitrogen were low at all temperatures. These results collectively indicate that water quality improvement activity of plant-mineral composites was dependent on the water temperature, and that the field application of above chemical during temperatures over $25^{\circ}C$, would be less effective in treating a cyanobacteria bloom dominated by Microcystis aeruginosa, than by diatoms.

• Journal of Korean Society on Water Environment
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• v.21 no.5
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• pp.470-476
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• 2005

The Korean Water Quality Index (K-WQI) was applied to the rivers located in the watershed of Daecheong Lake to assess the status of river water quality, and propose potential target constituents for better water quality management in the watershed. The estimated K-WQI value for each river was varied from 70 to 90, and Youngdongcheon showed the worst score while Mujunamdeachen showed the best score. The total nitrogen (TN) and total coliform bacteria were identified as the most significant constituents that degrade the K-WQI values in the rivers. The correlation coefficients (r) were determined between K-WQI and the delivered specific load ($kg/km^2/yr$) of BOD, TN, and TP to justify potential target constituents that have a great influence on the improvement of K-WQI values. The results showed that TN (r=-0.86) and TP (r=-0.85) have a strong negative relationships with K-WQI, but BOD have almost no effect. This implies that BOD, the surrogate parameter for organic pollutants, is no more a feasible water quality variable for the water quality management in the study site.

• Journal of Environmental Health Sciences
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• v.31 no.5 s.86
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• pp.398-403
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• 2005

This study is undertaken to find the optimal method to make the decision on the degree of water pollution by comparison of K-WQI, KOE-WQI that is made for index with the water quality index and water quality environment standard of the Frame Act on Environment Policy as the result of survey for water quality reality on the major point of the Yeongsan river from 2002 to 2004. The water quality of major rivers has some differences depending on seasons. however, under the water quality standard by the $BOD_5$ density, most of rivers displayed the water quality level of $II{\sim}III$ grading, and on K-WQI that is classified by indexing for 10 categories of pH, DO, $BOD_5,\;COD,\;SS,\;T-N,\;NH_3-N,\;NO_{3^-}$ N, T-P, and E-Coli and classified into 5 groups from 100 points to 40 points, they displayed the score distribution of the first grade in water quality for $85{\sim}100$ points to the second grade in water quality for $70{\sim}84$ points. On KOE-WQI that is classified by indexing for 5 categories of pH, DO, $BOD_5$, COD and T-coli and classified into 5 groups from 90 points or above for outstanding and 29 points or below for very bad, and the water quality distribution is made ranged from the first grade in water quality for 90 points or more to the third grade in water quality for $69{\sim}50$ points. In addition, for the contribution of the water quality decline, the Environmental standard has significant dependency on the $BOD_5$ density, with K-WQI contributing in various water quality decline depending on the environment around the river area of $BOD_5,\;T-N,\;NH_3-N,\;NO_3-N,\;T-P$, and E-Coli, and KOE-WQI acting os the factor contributing to lower the water quality decline by $BOD_5$, COD, and T-coli. As such, the current water quality environment standard has high dependency on $BOD_5$ and KOE-WQI excludes some nitrogen and phosphorus that considers the river environment that the grade in water quality is set by some category, and K-WQI reflected well of the ecology environment of rivers with the diversity of the assessment factor as well as to have the low dependency of specific factor to be objective.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.24-35
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• 2023

Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.

• Journal of Marine Life Science
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• v.7 no.1
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• pp.21-28
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• 2022

Benthic animals are important indicators in benthic environmental quality assessment. This study investigated the environmental characteristics and the distribution pattern of benthic animals, and assessed the benthic ecosystem using AMBI (AZTI's marine biotic index) and WQI (water quality index) in the subtidal zone of Muan bay. Samplings were collected from 10 stations in the subtidal zone of Muan bay on summer. In the upper area of Muan bay, grain size was finer and organic content was higher than those of in the lower area. The pollution indicator organism such as Musculista senhousia, Theora fragilis and Lumbrineris longifolia were dominant at some stations. The benthic community was distinguished into three groups of upper, center and lower area of Muan bay, and which were coincided with the results by correlation analysis between organic matter content and benthic health assessment (WQI and AMBI). As a result of this study, the health condition of the subtidal zone in Muan bay were good. However, from the results that benthic animals were not evenly distributed, and also the opportunistic species appeared, the load of organic matter in Muan bay seems to be increasing.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.748-756
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• 2006

Accurate and timely information on status and trends in the environment is necessary to shape sound water quality management policy and to implement water quality improvement programs efficiently. One of the most effective ways to communicate information on water quality trends to policy-makers, scientists, and the general public is with comprehensive water quality indices. The derivation and structure of a water quality index (WQI) for the classification of surface water quality is discussed. The WQI generally developed through the selection, transformation and weighting of determinants with rating curves based on legal standards and quality directives or guidelines. The representative pollutants should be included in the index, and the relationship between the quantity of these pollutants in the water and the resulting quality of the water should be based on scientific results. The WQI be simply and meaningfully formulated that nonscientifically trained users can easily become familiar with the framework of the system and use the output data to evaluate their own pollution problems.

• Journal of Korean Society on Water Environment
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• v.37 no.2
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• pp.114-127
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• 2021

Water pollution in the lower reaches of the Namhan River is getting worse due to drought and a decrease in water quantity due to climatic changes and hence is affecting the water quality of Paldang Lake. Accordingly, we have used a water quality index (WQI) and statistical analysis in this study to identify the characteristics of the water quality in the lower reaches of the Namhan River, the main causes of water pollution, and tributaries that need priority management. Typically, 10 items (WT, pH, EC, DO, BOD, COD, SS, T-N, T-P, and TOC) were used as the water quality factors for the statistical analysis, and the matrix of data was set as 324 × 10·1. The correlation analysis demonstrated a strong correlation between Chemical Oxygen Demand (COD) and T-P with a high statistical significance (r=0.700, p<0.01). Furthermore, the result of principal component analysis (PCA) revealed that the main factors affecting the change in water quality were T-P and organic substances introduced into the water by rainfall. Based on the Mann-Kendall test, a statistically significant increase in pH was observed in SH-1, DL, SH-2, CM, and BH, along with an increase in WQI in SH-2 and SM. BH was identified as a tributary that needs priority management in the lower reaches of the Namhan River, with a "Somewhat poor" (IV) grade in T-P, "Fair" grade in WQI, and "Marginal" grade in summer.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.221-234
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• 2016

In Korea, most industrial parks and major cities are located in coastal areas, which results in serious environmental problems in both coastal land and ocean. In order to effectively manage such problems especially in coastal ocean, water quality should be monitored. As there are many factors that influence water quality, the Korean Government proposed an integrated Water Quality Index (WQI) based on in situmeasurements of ocean parameters(bottom dissolved oxygen, chlorophyll-a concentration, secchi disk depth, dissolved inorganic nitrogen, and dissolved inorganic phosphorus) by ocean division identified based on their ecological characteristics. Field-measured WQI, however, does not provide spatial continuity over vast areas. Satellite remote sensing can be an alternative for identifying WQI for surface water. In this study, two schemes were examined to estimate coastal WQI around Korea peninsula using in situ measurements data and Geostationary Ocean Color Imager (GOCI) satellite imagery from 2011 to 2013 based on machine learning approaches. Scheme 1 calculates WQI using estimated water quality-related factors using GOCI reflectance data, and scheme 2 estimates WQI using GOCI band reflectance data and basic products(chlorophyll-a, suspended sediment, colored dissolved organic matter). Three machine learning approaches including Random Forest (RF), Support Vector Regression (SVR), and a modified regression tree(Cubist) were used. Results show that estimation of secchi disk depth produced the highest accuracy among the ocean parameters, and RF performed best regardless of water quality-related factors. However, the accuracy of WQI from scheme 1 was lower than that from scheme 2 due to the estimation errors inherent from water quality-related factors and the uncertainty of bottom dissolved oxygen. In overall, scheme 2 appears more appropriate for estimating WQI for surface water in coastal areas and chlorophyll-a concentration was identified the most contributing factor to the estimation of WQI.