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Comparison of the performance of a hydrologic model and a deep learning technique for rainfall-runoff analysis

Tropical Cyclone Research and Review 2021年第4期10卷 215-222页

作者： chorong kim Chung-Soo kim Department of Hydro Science and Engineering Research Korea Institute of Civil Engineering and Building TechnologyGoyang-siGyeonggi-doRepublic of Korea

Rainfall-runoff analysis is the most important and basic analysis in water resources management and *** rainfall-runoff analysis methods generally have used hydrologic ***-runoff analysis should consider complex interactions in the water cycle process,including precipitation and *** this study,rainfall-runoff analysis was performed using a deep learning technique that can capture the relationship between a hydrological model used in the existing methodology and the data *** study was conducted in the Yeongsan River basin,which forms a large-scale agricultural area even after industrialization,as the study *** the hydrology model,SWAT(Soil and Water Assessment Tool)was used,and for the deep learning method,a Long Short-Term Memory(LSTM)network was used among RNNs(Recurrent Neural Networks)mainly used in time series *** a result of the analysis,the correlation coefficient and NSE(Nash-Sutcliffe Efficiency),which are performance indicators of the hydrological model,showed higher performance in the LSTM *** general,the LSTM network performs better with a longer calibration *** other words,it is worth considering that a data-based model such as an LSTM network will be more useful than a hydrological model that requires a variety of topographical and meteorological data in a watershed with sufficient historical hydrological data.

关键词： Rainfall-runoff analysis LSTM SWAT Deep learning

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Analysis of AI-based techniques for forecasting water level according to rainfall

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Tropical Cyclone Research and Review 2021年第4期10卷 223-228页

作者： chorong kim Chung-Soo kim Department of Hydro Science and Engineering Research Korea Institute of Civil Engineering and Building TechnologyGoyang-siGyeonggi-doRepublic of Korea

Water level forecasting according to rainfall is important for water resource management and disaster *** hydrological analysis is accompanied by difficulties in water level forecasting analysis such as topographic data and model parameter optimization of the ***,with the improvement of AI(Artificial Intelligence)technology,a research using AI technology in the water resource field is being *** this research,water level forecasting was performed using an AI-based technique that can capture the relationship between *** the watershed for the study,the Seolmacheon catchment which has the rich historical hydrological data,was ***(Support Vector Machine)and a gradient boosting technique were used for AI machine *** AI deep learning,water level forecasting was performed using a Long Short-Term Memory(LSTM)network among Recurrent Neural Networks(RNNs)used for time series *** correlation coefficient and NSE(Nash-Sutcliffe Efficiency),which are mainly used forhydrological analysis,were used as performance *** a result of the analysis,all three techniques performed excellently in water level *** them,the LSTM network showed higher performance as the correction period using historical data *** there is a concern about an emergency disaster such as torrential rainfall in Korea,water level forecasting requires quick *** is thought that the above requirements will be satisfied when an AI-based technique that can forecast water level using historical hydrology data is applied.

关键词： Water level forecasting SVM Gradient boosting RNN LSTM

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Effects of irradiation on superconducting properties of small-grained MgB_(2) thin films

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Chinese Physics B 2023年第12期32卷 534-540页

作者：刘丽 Jung Min Lee Yoonseok Han Jaegu Song chorong kim Jaekwon Suk Won Nam Kang 刘杰 Soon-Gil Jung Tuson Park Center for Quantum Materials and Superconductivity(CQMS) Sungkyunkwan UniversitySuwon 16419Republic of Korea Department of Physics Sungkyunkwan UniversitySuwon 16419Republic of Korea Institute of Modern Physics Chinese Academy of Sciences(CAS)Lanzhou 730000China School of Nuclear Science and Technology University of Chinese Academy of Sciences(UCAS)Beijing 100049China Korea Multi-purpose Accelerator Complex Korea Atomic Energy Research InstituteGyeongjuGyeongbuk 38180Republic of Korea Department of Physics Education Sunchon National UniversitySuncheon 57922Republic of Korea

We investigate the effect of ion irradiation on MgB_(2) thin films with small grains of approximately 122 nm and 140 *** flux pinning by grain boundaries is insignificant in the pristine MgB_(2) films due to good inter-grain connectivity,but is significantly improved after 120-keV Mn-ion *** scaling behavior of the flux pinning force density for the ion-irradiated MgB_(2) thin films with nanoscale grains demonstrates the predominance of pinning by grain boundaries,in contrast to the single-crystalline MgB_(2) films where normal point pinning was dominant after low-energy ion *** results suggest that irradiation-induced defects can accumulate near the grain boundaries in metallic MgB_(2) superconductors.

关键词： MgB_(2)films grain boundaries flux pinning low-energy ion irradiation

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