• 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 the Korean association of regional geographers
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• v.2 no.1
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• pp.51-67
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• 1996

There are three main rice-growing regions in the United States: the prairie region along the Mississippi River Valley in eastern Arkansas; the Gulf Coast prairie region in southwestern Louisiana and southeastern Texas; and the Central Valley of California. The Central Valley of California is producing about 23% of the US rice(Fig. 1). In California. most of the crop has been produced in the Colusa, Sutter, Butte, Glenn Counties of the Sacramento Valley since 1912, when rice was commercially grown for the first time in the state(Fig. 2). Roughly speaking, the average annual area sown to rice in California is about 300,000 acres to 400,000 acres during the last forty years(Fig. 3). California rice is grown under a Mediterranean climate characterized by warm, dry, clear days, and a long growing season favorable to high photosynthetic rates and high rice yields. The average rice yield per acre is probably higher in California than in any other rice-growing regions of the world(Fig. 4). A dependable supply of irrigation water must be available for a successful rice culture. Most of the irrigation water for California rice comes from the winter rain and snow-fed reservoir of the Sierra Nevada mountain ranges. Less than 10 percent of rice irrigation water is pumped from wells in areas where surface water is not sufficient. It is also essential to have good surface drainage if maximum yields are to be produced. Rice production in California is highly mechanized, requiring only about four hours of labor per acre. Mechanization of rice culture in California includes laser-leveler technology, large tractors, self-propelled combines for harvesting, and aircraft for seeding, pest control, and some fertilization. The principal varieties grown in California are medium-grain japonica types with origins from the cooler rice climates of the northern latitudes (Table 1). Long-grain varieties grown in the American South are not well adapted to California's cooler environment. Nearly all the rice grown recently in California are improved into semidwarf varieties. Choice of variety depends on environment, planting date, quality desired, marketing, and harvesting scheduling. The Rice Experiment Station at Biggs is owned, financed, and administered by the rice industry. The station was established in 1912, as a direct result of the foresight and effort of Charles Edward Chambliss of the United States Department of Agriculture. Now, The station's major effort is the development of improved rice varieties for California.

• Journal of the Korean Geographical Society
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• v.42 no.3 s.120
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• pp.368-387
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• 2007

In order to explain geomorphological characteristics of the Korean Peninsula, it is necessary to understand the spatial distribution of tectonic movements and its causes. Even though geomorphological elements which might have been formed by tectonic movements(e.g. tilted overall landform, erosion surface, river terrace, marine terraces, etc.) have long been considered as main geomorphological research topics in Korea, the knowledge on the spatial distribution of tectonic movement is still limited. This research aims to identify the spatial distributions of tectonic movement via sequential analyses of Digital Elevation Model(DEM). This paper first developed a set of terrain analysis techniques derived from theoretical interrelationships between tectonic uplifts and landsurface denudation processes. The terrain analyses used in this research assume that elevations along major drainage basin divides might preserve original landsurfaces(psuedo-landsuface) that were formed by tectonic movement with relatively little influence by denudation processes. Psuedo-landsurfaces derived from a DEM show clear spatial distribution patterns with distinct directional alignments. Lines connecting psuedo-landsufaces in a certain direction are defined as psuedo-landsurface axes, which are again categorized into two groups: the first is uplift psuedo-landsurface axes that indicate the axis of landmass uplift; and the second is denudational psuedo-landsurface axes that cross step-shaped pusedo-landsurfaces formed via surface denudation. In total, 13 axes of pusedo-landsurface are identified in the Korean Peninsula, which show distinct direction, length, and relative uplift rate. Judging from the distribution of psudo-landsurfaces and their axes, it is concluded that the Korean Peninsula ran be divided into four tectonic regions, which are named as the Northern Tectonic Region, Center Tectonic Region, Southern Tectonic Region, and East Sea Tectonic Region, respectively. The Northern Tectonic Region had experienced a regional uplift centered at the Kaema plateau, and the rate of uplift gradually decreased toward southern, western and eastern directions. The Center Tectonic Region shows an arch-shaped uplift. Its uplift rate is the highest along the East Sea and the rate decreases towards the Yellow sea. The Southern Tectonic Region shows an asymmetric uplift centered a line connecting Dukyu and Jiri Mountains in the middle of the region. The eastern side of the Southern Regions shows higher uplift rate than that of the western side. The East Sea Tectonic Region includes south-eastern coastal area of the peninsula and Gilju-Myeongchun Jigudae, which shows relatively recent tectonic movements in Korea. Since this research visualizes the spatial heterogeneity of long-term tenonic movement in the Korean peninsula, this would provide valuable basic information on long-term and regional differences of geomorphological evolutionary processes and regional geomorphological differences of the Korean Peninsula.

• Journal of Wetlands Research
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• v.25 no.1
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• pp.35-47
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• 2023

Korean facility horticulture and hydroponic cultivation methods increase, requiring the management of waste water generated. In this study, the amount of fertilizer contained in the discharged waste liquid was determined. By evaluating this as a price, it was suggested to reduce water treatment costs and recycle fertilizer components. It was evaluated based on the results of major water quality analysis of waste liquid by crop, such as tomatoes, paprika, cucumbers, and strawberries, and in the case of P component, it was analyzed by converting it to the amount of phosphoric acid (P₂O₅). The amount of nitrogen (N) can be calculated by discharging 1,145.90kg·ha^-1 of tomatoes, 920.43kg·ha^-1 of paprika, 804.16kg·ha^-1 of cucumbers, 405.83kg·ha^-1 of strawberries, and the fertilizer content of P₂O₅ is 830.65kg·ha^-1 of paprika, 622.32kg·ha^-1 of tomatoes, 477.67kg·ha^-1 of cucumbers. In addition, trace elements such as potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), and manganese (Mn) were also analyzed to be emitted. The price per kg of each item calculated by averaging the price of fertilizer sold on the market can be evaluated as KRW, N 860.7, P 2,378.2, K 2,121.7, Ca 981.2, Mg 1,036.3, Fe 126,076.9, Mn 62,322.1, Zn 15,825.0, Cu 31,362.0, B 4,238.0, Mo 149,041.7. The annual fertilizer loss amount for each crop was calculated by comprehensively considering the price per kg calculated based on the market price of fertilizer, the concentration of waste by crop analyzed earlier, and the average annual emission of hydroponic cultivation. As a result of the analysis, the average of the four hydroponic crops was 5,475,361.1 won in fertilizer ingredients, with tomatoes valued at 6,995,622.3 won, paprika valued at 7,384,923.8 won, cucumbers valued at 5,091,607.9 won, and strawberries valued at 2,429,290.6 won. It was expected that if hydroponic drainage is managed through self-treatment or threshing before discharge rather than by leaking it into a river and treating it as a pollutant, it can be a valuable reusable fertilizer ingredient along with reducing water treatment costs.