• Journal of Soil and Groundwater Environment
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• v.19 no.3
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• pp.123-133
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• 2014

It is important to predict the groundwater level fluctuation for effective management of groundwater monitoring system and groundwater resources. In the present study, three different time series models for the prediction of groundwater level in response to rainfall were built, those are transfer function noise model (TFNM), artificial neural network (ANN), and adaptive neuro fuzzy interference system (ANFIS). The models were applied to time series data of Boen, Cheolsan, and Hongcheon stations in National Groundwater Monitoring Network. The result shows that the model performance of ANN and ANFIS was higher than that of TFNM for the present case study. As lead time increased, prediction accuracy decreased with underestimation of peak values. The performance of the three models at Boen station was worst especially for TFNM, where the correlation between rainfall and groundwater data was lowest and the groundwater extraction is expected on account of agricultural activities. The sensitivity analysis for the input structure showed that ANFIS was most sensitive to input data combinations. It is expected that the time series model approach and results of the present study are meaningful and useful for the effective management of monitoring stations and groundwater resources.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.135-142
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• 2022

Recently, with the development of data processing technology and the increase of computational power, methods to solving social problems using Artificial Intelligence (AI) are in the spotlight, and AI technologies are replacing and supplementing existing traditional methods in various fields. Meanwhile in Korea, heavy rain is one of the representative factors of natural disasters that cause enormous economic damage and casualties every year. Accurate prediction of heavy rainfall over the Korean peninsula is very difficult due to its geographical features, located between the Eurasian continent and the Pacific Ocean at mid-latitude, and the influence of the summer monsoon. In order to deal with such problems, the Korea Meteorological Administration operates various state-of-the-art observation equipment and a newly developed global atmospheric model system. Nevertheless, for precipitation nowcasting, the use of a separate system based on the extrapolation method is required due to the intrinsic characteristics associated with the operation of numerical weather prediction models. The predictability of existing precipitation nowcasting is reliable in the early stage of forecasting but decreases sharply as forecast lead time increases. At this point, AI technologies to deal with spatio-temporal features of data are expected to greatly contribute to overcoming the limitations of existing precipitation nowcasting systems. Thus, in this project the dataset required to develop, train, and verify deep learning-based precipitation nowcasting models has been constructed in a regularized form. The dataset not only provides various variables obtained from multiple sources, but also coincides with each other in spatio-temporal specifications.

• Journal of Korea Water Resources Association
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• v.46 no.7
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• pp.697-706
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• 2013

In recent, Korea has faced on water quality management problems in reservoir and river because of increasing water temperature and rainfall frequency caused by climate change. This study is effectively to manage water quality for establishment of algal bloom forecasting models with artificial neural network. Daecheong reservoir located in Geum river has suitable environment for algal bloom because it has lots of contaminants that are flowed by rainfall. By using back propagation algorithm of artificial neural networks (ANNs), a model has been built to forecast the algal bloom over short-term (1, 3, and 7 days). In the model, input factors considered the hydrologic and water quality factors in Daecheong reservoir were analyzed by cross correlation method. Through carrying out the analysis, input factors were selected for algal bloom forecasting model. As a result of this research, the short term algal bloom forecasting models showed minor errors in the prediction of the 1 day and the 3 days. Therefore, the models will be very useful and promising to control the water quality in various rivers.

• Journal of Environmental Science International
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• v.19 no.7
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• pp.843-848
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• 2010

This study focused on the characteristics of change soil water with respect to soil thickness and soil mixture ratio, in order to effectively carry out an afforestation system for a roof with a low level of management and a light weight. Soil hardness tended to increase as sand particle was increase regardless soil thickness and soil porosity had more higher artificial soil than natural soil mixture. In case of soil pH, natural soil mixture had between 6.7 and 7.4, and artificial soil mixture had 6.0~6.8. Organic matter, electrical conductance and exchangeable content were highest in $L_{10}$, which it had the highest leafmold ratio. Soil moisture tension(kPa) in 15cm soil thickness was observed natural soil mixture had a considerable change but artificial soil mixture had a gradual change when non-rainfall kept on. In the experimental $L_{10}$, $S_{10}$, $S_7L_3$ and $S_5L_5$ object, the amount of moisture tended to rapidly decrease. However, in the experimental $P_7P_1L_2$, $P_6P_2L_2$, $P_5P_3L_2$ and $P_4P_4L_2$ objects, which contained pearlite and peat moss, the amount of moisture tended to gradually decrease. As a result, the use of a artificial soil mixture soil seems to be required for the afforestation of a roof for a low level of management.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.345-352
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• 2020

Securing the lead time for evacuation is crucial to minimize flood damage. In this study, downstream water levels for heavy rainfall were predicted using measured water level observation data. Multiple regression analysis and artificial neural networks were applied to the Seom River experimental watershed to predict the water level. Water level observation data for the Seom River experimental watershed from 2002 to 2010 were used to perform the multiple regression analysis and to train the artificial neural networks. The water level was predicted using the trained model. The simulation results for the coefficients of determination of the artificial neural network level prediction ranged from 0.991 to 0.999, while those of the multiple regression analysis ranged from 0.945 to 0.990. The water level prediction model developed using an artificial neural network was better than the multiple-regression analysis model. This technique for forecasting downstream water levels is expected to contribute toward flooding warning systems that secure the lead time for streams.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• Journal of the Korean GEO-environmental Society
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• v.17 no.5
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• pp.27-36
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• 2016

Low Impact Development (LID) techniques are eco-friendly storm water management process for water circulation restoration and non-point pollutant reduction. In this study, four LID techniques (Small constructed wetland, Infiltration trench box, Infiltration trench, Vegetated swale) were selected and installed as a real size at the real site. All facilities were evaluated as monitoring under the real environmental climate situation and an artificial rain with exceeding design rainfall. In various rainfall, runoff reduction efficiency and non-point pollutant removal efficiency are increased to the bigger Surface Area of LID (SA)/Catchment Area (CA) ratio and the bigger Storage Volume of LID (SV)/Catchment Area (CA) ratio. Runoff did not occur at all rainfall event (max. 17.2 mm) in infiltration trench and vegetated swale. But Small constructed wetland was more efficient at less than 10 mm, a efficiency of infiltration trench box was similar at different rainfall. Although different conditions (such as structural material of LID, rainfall flow rate, antecedent dry periods), LID techniques are good effects not only water circulation improvement but also water quality improvement.

• Water for future
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• v.25 no.3
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• pp.105-113
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• 1992

The purpose of this paper is to introduce and to apply the artificial neural network theory to real hydrologic system for forecasting daily streamflows during flood periods. The hydrologic dynamic process of rainfall-runoff is identified by the iterated estimation of system parameters that are determined by adjusting the weights of the network according to the non-linear response characteristics which is formed the model. Back propagation algorithm of neural network model is applied for the estimation of system parameters with past daily rainfall and runoff series data, and streamflows are forecasted using the parameters. The forecasted results are analyzed by statistical methods for the comparison with the observed.

• Journal of Korean Society on Water Environment
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• v.24 no.5
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• pp.611-617
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• 2008

It is necessary to analysis the data characteristics of discharge and water quality for efficient water resources management, aggressive alternatives to inundation by flood and various water pollution accidents, the basic information to manage water quality in lakes and to make environmental policy. Therefore, the present study applied Self-Organizing Map (SOM) showing excellent performance in classifying patterns with weights estimated by self-organization. The result revealed five patterns and TOC versus rainfall-storage data according to the respective patterns were depicted in two-dimensional plots. The visualization presented better understanding of data distribution pattern. The result in the present study might be expected to contribute to the modeling procedure for data prediction in the future.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.65 no.4
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• pp.353-364
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• 2020

Recently in Korea, soybean harvesting has been delayed due to rainfall during the harvesting season, resulting in a reduction in yield and seed quality. This study was conducted to analyze the changes in yield and seed quality during delayed harvest with rainfall treatment using different harvesting methods, including field harvesting and polyethylene film covering after cutting fully-matured soybean plants (PE covering after cutting), with two major Korean soybean cultivars (Glycine max L), Pungsannamulkong and Daewonkong. The shattering rate of Pungsannamulkong, which is higher than that of Daewonkong, increased up to 41.8% when the harvest was delayed for 40 days without rainfall treatment by harvesting with PE covering after cutting. The weight of 100 seeds tended to decrease slightly as harvesting was delayed. When Daewonkong was harvested using the PE covering after cutting method with rainfall treatment, the yield decreased to the lowest level with a 0.8 kg ha^-1 daily reduction rate. Pungsannamulkong showed the lowest yield when harvested using PE covering after cutting without rainfall treatment with a 3.4 kg ha^-1 daily reduction rate. The infected seed rate increased according to the harvest delay in both cultivars, and significant differences were observed according to rainfall treatment and harvesting method. The germination rate was maintained above 95% even after 40 days of delayed harvest if there was no rainfall treatment. However, with rainfall treatment, the germination rate was significantly lowered as harvesting time was delayed. In the field harvesting with rainfall treatment, the germination rate decreased to 77.2% for Daewonkong and 76.5% for Pungsannamulkong after 40 days of harvest delay. For the 100-seed weight, effects of individual treatments and interactions between treatments were not observed. In contrast, the effect of interactions between treatments on the shattering rate was significant in both cultivars, indicating that the shattering rate had the greatest impact on the yield changes during delayed harvest.