• Title/Summary/Keyword: flood flow characteristics

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Spatial Distribution and Successional Changes of Riparian Vegetation on Sandbars Exposed after Watergate-Opening of Weirs in the Geumgang River, South Korea (보 개방 후 노출된 금강 모래톱에서 하천 식생의 공간 분포와 천이)

  • Lee, Cheolho;Kim, Hwirae;Cho, Kang-Hyun
    • Ecology and Resilient Infrastructure
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    • v.9 no.3
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    • pp.194-205
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    • 2022
  • Sandbars formed by sediment transportation and sedimentation are some of the most important habitats for specific wildlife and they provide an aesthetic landscape in streams. The purpose of this study was to understand the successional process of the colonization and development of early vegetation over time on sandbars exposed by the opening of a gate at a downstream weir. We selected the following four study sites in the Geumgang River, South Korea: three weir-upstream sites with different gate-opening times and a control site that was not affected by weir operation. Changes in the structural characteristics and spatial distribution of the riparian vegetation on the sandbars exposed after opening the gate at the weir were surveyed according to the different exposure periods of the sandbars at the study sites. The newly formed sandbars accounted for more than 33% of the area of the existing floodplain in the three weir-upstream sites of the Geumgang River after opening the gate at the weir. Nine main plant communities were distributed on the exposed sandbars. These communities were classified as annual mesophytic, perennial hydrophytic, perennial hygrophytic, subtree, and tree vegetation based on their species traits. As the duration of exposure of the sandbar increased, the area of the bare sandbar and the annual herbaceous and perennial hydrophytic communities decreased, and the areas occupied by perennial hygrophytic, subtree, and tree communities increased. Changes in vegetation on the sandbar were classified into three types of succession according to the condition of the aquatic habitat before the gate-opening and the degree of physical disturbance caused by the water flow after the gate-opening. The types of succession were: 1) succession starting from hydrophytes in the lentic aquatic zone, 2) succession starting from annual herbaceous hygrophytes in the lotic aquatic zone, and 3) willow-dominated succession in the disturbed channel side. Our results suggested that the dynamics of successional changes in vegetation should be considered during weir operation to ecologically manage the habitats and landscape of the fluvial landforms, including sandbars in streams.

A Study on Knowledge Entity Extraction Method for Individual Stocks Based on Neural Tensor Network (뉴럴 텐서 네트워크 기반 주식 개별종목 지식개체명 추출 방법에 관한 연구)

  • Yang, Yunseok;Lee, Hyun Jun;Oh, Kyong Joo
    • Journal of Intelligence and Information Systems
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    • v.25 no.2
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    • pp.25-38
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    • 2019
  • Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.