• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.261-272
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• 2023

In recent years, natural disasters such as heavy rainfall and typhoons have occurred more frequently, and their severity has increased due to climate change. The Korea Meteorological Administration (KMA) currently uses the same criteria for all regions in Korea for watch and warning based on the maximum cumulative rainfall with durations of 3-hour and 12-hour to reduce damage. However, KMA's criteria do not consider the regional characteristics of damages caused by heavy rainfall and typhoon events. In this regard, it is necessary to develop new criteria considering regional characteristics of damage and cumulative rainfalls in durations, establishing four stages: blue, yellow, orange, and red. A classification model, called DSCM (Disaster Severity Classification Model), for the four-stage disaster severity was developed using four machine learning models (Decision Tree, Support Vector Machine, Random Forest, and XGBoost). This study applied DSCM to local governments of Seoul, Incheon, and Gyeonggi Province province. To develop DSCM, we used data on rainfall, cumulative rainfall, maximum rainfalls for durations of 3-hour and 12-hour, and antecedent rainfall as independent variables, and a 4-class damage scale for heavy rain damage and typhoon damage for each local government as dependent variables. As a result, the Decision Tree model had the highest accuracy with an F1-Score of 0.56. We believe that this developed DSCM can help identify disaster risk at each stage and contribute to reducing damage through efficient disaster management for local governments based on specific events.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Journal of Forest and Environmental Science
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• v.3 no.1
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• pp.1-9
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• 1983

The purpose of this study was to elucidate the ecophysiological habitat of natural Sorbus commixta forest at Mt. Seorak. The results obtained were as follows: 1. The Sorbus commixta trees mainly distributed from 900m to 1,500m altitude. In there, the warm index(WI) was about 42$3.2{\times}10^3$ to $9.2{\times}10^3$, cation exchange capacity(CEC) was 13.7 to 19.5mg/100g, N content 0.21 to 0.39%, $P_2O_5$ content was 22.6 to 38.7ppm, and pH value was 5.6 to 5.8 respectively. 4. The upper crown trees in Sorbus commixta communities were Abies nephrolepis, Taxus cuspidata, Betula platyphylla var. japonica, Quercus${\times}$grosseserrata, Acer mono, Prunus sargentii, Carpinus cordata, Tilia amurensis, and the under crown trees were Rhododendron brachycarpum, Acer pseudo-sieboldianum, Thuja olientalis, Corylus heterohpylla, Philadelphus schrenckii, Rhododendron schlippenbachii, Rhododendron mucronulatum, and Magnolia sieboldii. 5. The stand densities were 1,156 trees/ha at 1,160m and 3,600 trees/ha at 1,300m respectively. The coverages by the DBH basal area were 0.37 at 1,160m and 0.31 at 1,300m respectively, and the vegetation coverages by the crown projection area were 2.04 at 1,160m and 1.61 at 1,300m respectively. 6. The light extinction coefficient(k) in Beer-Lambert's law, showed the distance, F(z), from top canopy to aboveground, was 0.17. 7. The water relations parameters of Sorbus commixta shoot were obtained by the pressure chamber technique. The osmotic pressure, ${\pi}_o$, at maximum turgor was -16.2 bar, and VAT pressure was 14.5bar. The osmotic pressure, ${\pi}_p$, at incipient plasmolysis was -19.4bar. The relative water contents at incipient plasmolysis were 83.1% ($v_p/v_o$) and 87.1%($v_p/w_s$;$w_s$, total water at maximum turgor). 8. The bulk modulus of elasticity(E) of shoot was about 69.6. The total symplasmic water to total water in shoot was 67.7%, and the apoplastic water to total water was 32.3%.

• Korean Journal of Ecology and Environment
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• v.45 no.2
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• pp.158-173
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• 2012

The objectives of this study were to analyze the longitudinal gradient and temporal variations of water quality in Daecheong Reservoir in relation to the major inflowing streams from the watershed, during 2001~2010. For the study, we selected 7 main-stream sites of the reservoir along the main axis of the reservoir, from the headwater to the dam and 8 tributary streams. In-reservoir nutrients of TN and TP showed longitudinal declines from the headwater to the dam, which results in a distinct zonation of the riverine ($R_z$, M1~M3), transition ($T_z$, M4~M6), and lacustrine zone ($L_z$, M7) in water quality, as shown in other foreign reservoirs. Chlorophyll-a (CHL) and BOD as an indicator of organic matter, were maximum in the $T_z$. Concentration of total phosphorus (TP) was the highest (8.52 $mg\;L^{-1}$) on March in the $R_z$, and was the highest (165 ${\mu}g\;L^{-1}$) in the $L_z$ on July. Values of TN was the maximum (377 ${\mu}g\;L^{-1}$) on August in the $R_z$, and was the highest (3.76 $mg\;L^{-1}$) in the $L_z$ on August. Ionic dilution was evident during September~October, after the monsoon rain. The mean ratios of TN : TP, as an indicator of limiting factor, were 88, which indicates that nitrogen is a surplus for phytoplankton growth in this system. Nutrient analysis of inflowing streams showed that major nutrient sources were headwater streams of T1~T2 and Ockcheon-Stream of T5, and the most influential inflowing stream to the reservoir was T5, which is located in the mid-reservoir, and is directly influenced by the waste-water treatment plants. The key parameters, influenced by the monsoon rain, were TP and suspended solids (SS). Empirical models of trophic variables indicated that variations of CHL in the $R_z$ ($R^2$=0.044, p=0.264) and $T_z$ ($R^2$=0.126, p=0.054) were not accounted by TN, but were significant (p=0.032) in the $L_z$. The variation of the log-transformed $I_r$-CHL was not accounted ($R^2$=0.258, p=0.110) by $I_w$-TN of inflowing streams, but was determined ($R^2$=0.567, p=0.005) by $I_w$-TP of inflowing streams. In other words, TP inputs from the inflowing streams were the major determinants on the in-reservoir phytoplankton growth. Regression analysis of TN : TP suggested that the ratio was determined by P, rather than N. Overall, our data suggest that TP and suspended solids, during the summer flood period, should be reduced from the eutrophication control and P-input from Ockcheon-Stream should be controlled for water quality improvement.