• Proceedings of the Korean Society of Agricultural Engineers Conference
• /
• 1999.10c
• /
• pp.486-492
• /
• 1999

One of the purpose of the reservoir operation is minimizing theinnudation area in the downstream reaches during flood period.l To execute the gate operation properly , it requires lots of real-time data such as rainfall, reservoir level, and water level in the downstrea. Gate operation model was developed with the flood discharge obtained from real-time flood forecasting model and the criterion prepared from the past history of gate operation. Water level in the downstream would be increased by the releasing discharge from the spillway and the area of paddy land flooded in a certain detph and time would be estimated usnig GIS map. Gate operation model was applied to the Yedang reservoir, and the flooded area, depth and time in the paddy land was estimaged.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.3
• /
• pp.76-92
• /
• 2018

This study aimed to establish database of rainfall inundation area by rainfall scenarios and conduct a real time prediction for urban flood mitigation. the data leaded model was developed for the mapping of inundated area with rainfall forecast data provided by korea meteorological agency. for the construction of data leaded model, 1d-2d modeling was applied to Gangnam area, where suffered from severe flooding event including september, 2010. 1d-2d analysis result agree with observed in term of flood depth. flood area and flood occurring report which maintained by NDMS(national disaster management system). The fitness ratio of the NDMS reporting point and 2D flood analysis results was revealed to be 69.5%. Flood forecast chart was created using pre-flooding database. It was analyzed to have 70.3% of fitness in case of flood forecast chart of 70mm, and 72.0% in case of 80mm flood forecast chart. Using the constructed pre-flood area database, it is possible to present flood forecast chart information with rainfall forecast, and it can be used to secure the leading time during flood predictions and warning.

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

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.1
• /
• pp.91-98
• /
• 2023

With the increasing flood damage by frequently localized heavy rains, flood prediction research are being conducted to prevent flooding damage in advance. In this paper, we present a machine-learning scheme for developing a flooding depth and location prediction model using real-time rainfall data. This scheme proposes a dataset configuration method using the data as input, which can robustly configure various rainfall distribution patterns and train the model with less memory. These data are composed of two: valid total data and valid local. The one data that has a significant effect on flooding predicted the flooding location well but tended to have different values for predicting specific rainfall patterns. The other data that means the flood area partially affects flooding refers to valid local data. The valid local data was well learned for the fixed point method, but the flooding location was not accurately indicated for the arbitrary point method. Through this study, it is expected that a lot of damage can be prevented by predicting the depth and location of flooding in a real-time manner.

• Water for future
• /
• v.27 no.3
• /
• pp.145-158
• /
• 1994

The linear reservoir rainfall-runoff system was developed as a rainfall-runoff event simulation model. It was achieved from large modification of runoff function method. There are six parameters in the model. Hydrologic losses consist of some quantity of initial loss and some ratio of rainfall intensity followed by initial loss. The model has analytical routing equations. Hooke and Jeeves algorithm was used to model calibration. Parameters were estimated for flood events from '84 to '89 at Seomyeon and Munmak stream gauges, and the trends of major parameters were analyzed. Using the trends, verifications were performed for '90 flood event. Because antecedent fainfalls affect initial loss, future researches are required on such effects. The estimation method of major parameters should also be studied for real-time forecasting.

• Journal of the Korean Geotechnical Society
• /
• v.25 no.5
• /
• pp.53-64
• /
• 2009

Many slope failures are induced by rainfall infiltration. A lot of recent researches are therefore focused on rainfall-induced slope instability and the rainfall infiltration is recognized as the important triggering factor. The rainfall infiltrates into the soil slope and makes the matric suction lost in the slope and even the positive pore water pressure develops near the surface of the slope. They decrease the resisting shear strength. In Korea, a few public institutions suggested conservative slope design guidelines that assume a fully saturated soil condition. However, this assumption is irrelevant and sometimes soil properties are misused in the slope design method to fulfill the requirement. In this study, a more relevant slope stability evaluation method is suggested to take into account the real rainfall infiltration phenomenon. Unsaturated soil properties such as shear strength, soil-water characteristic curve and permeability for Korean weathered soils were obtained by laboratory tests and also estimated by artificial neural network models. For real-time assessment of slope instability, failure warning criteria of slope based on deterministic and probabilistic analyses were introduced to complement uncertainties of field measurement data. The slope stability evaluation technique can be combined with field measurement data of important factors, such as matric suction and water content, to develop an early warning system for probably unstable slopes due to the rainfall.

• Water for future
• /
• v.29 no.4
• /
• pp.149-160
• /
• 1996

The parameters of the storage function model (SFM) are taken as constants, while they have different values every rainfall events and time of the runoff. Therefore, the results of the SFM show remarkably large errors in general. In this study, the modified sorage function model (MSFM), in which the time variant parameters are introduced, is proposed to improve the SFM which is a conceptual rainfall-runoff model. The fuzzy reasoning is applied as a real-time control method of the time-variant parameters of the proposed model. The applicability of the MSFM was examined in the Bochung river, a tributary of Geum river in Korea. The pattern of predicted outflow hydrograph and peak outflow by the MSFM with fuzzy control are much similar to the measured values in comparison with the results produced by the SFM.

• Journal of Korean Society on Water Environment
• /
• v.20 no.5
• /
• pp.494-499
• /
• 2004

Growth in population and urbanization has progressively increased the loadings of pollutants from non-point sources as well as point sources. Therefore, it is necessary to manage both point and non-point sources contaminations for protecting water environment and improving water quality. This study investigated the characteristics of pollutant release over a wide range of rainfall intensities as a requisite to control road runoff that accounts for the largest portion of non-point source contamination in urban areas. Samples of runoff rainwater collected from real road surfaces were analyzed for physicochemical parameters such as pH, suspended solids, and heavy metals. A experimental model road ($30cm{\times}30cm$) was also used to evaluate wash-off properties of pollutants deposited on the surface as functions of time and rainfall intensity. Analysis of runoff samples on rain events showed that the pollutant wash-off patterns for heavy metal and suspended solids were similar. This implies that the particles in rainwater adsorb heavy metals. Experiments using the model road made of impervious asphalt demonstrate a strong first flush phenomenon. At high rainfall intensity, approximately 80% of total pollutants were released within 15 min. The pollutant wash-off rates rapidly increase from 9 mm/hr to 12 mm/hr of rainfall intensity and decrease over 12 mm/hr of rainfall intensity.

• Journal of Korea Water Resources Association
• /
• v.51 no.9
• /
• pp.813-826
• /
• 2018

Recently, weather radar system has been widely used for effectively monitoring near real-time weather conditions. The radar rainfall estimates are generally relies on the Z-R equation that is an indirect approximation of the empirical relationship. In this regards, the bias in the radar rainfall estimates can be affected by spatial-temporal variations in the radar profile. This study evaluates the uncertainty of the Z-R relationship while considering the rainfall types in the process of estimating the parameters of the Z-R equation in the context of stochastic approach. The radar rainfall estimates based on the Bayesian inference technique appears to be effective in terms of reduction in bias for a given season. The derived Z-R equation using Bayesian model enables us to better represent the hydrological process in the rainfall-runoff model and provide a more reliable forecast.

• Proceedings of the Korean Society of Agricultural Engineers Conference
• /
• 1998.10a
• /
• pp.311-317
• /
• 1998

Recently irrigation reservoir has been developed to perform multipurpose function. To get a maximum effect it requires to establish optimal management system for irrigation reservoir in drought and flood season. Especially we dealt with optimal flood control system for irrigation reservoir in this study. This system consists of real-time rainfall data via online system, real-time flood forecasted by SCS method in hourly basis, storage volume by water balance equation, optimal releasing discharge from the gate, the water level in right downstream, and calculation of innundated area, depth, and time using GIS, and amount of flood damages. If we consider the relation of these sub module reasonably, we can reach the optimal flood control to minimize flood damage