• Water Engineering Research
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• v.3 no.2
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• pp.113-122
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. 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 lifetime, 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. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.

• Solar Energy
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• v.9 no.3
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• pp.81-91
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• 1989

This study represents the method to predict the flow duration curve and primary design specifications of small hydropower plant at hydropower site through analyzing the monthly rainfall data. Weibull distribution was selected to characterize the rainfall data and Thiessen method was used to calculate monthly average flowrate at site. Application of these results, primary design specifications such as design flowrate, annual average load factor and utility factor, annual average hydropower density and annual electric energy production were estimated and discussed for surveyed site located in Daigi-ri, Kangwon province. And performance characteristic model of small hydro-power plant was applied to estimate these specifications.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.2 no.1
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• pp.29-36
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• 1998

An instantaneous unit sediment graph (IUSG) model is investigated for prediction of sediment yield from an upland watershed in Northwestern Mississippi. Sediment yields are predicted by convolving source runoff with an IUSG. The IUSG is the distribution of sediment from an instantaneous burst of rainfall producing one unit of runoff. The IUSG, defined as a product of the sediment concentration distribution (SCD) and the instantaneous unit hydrograph (IUH), is known to depend on the characteristics of the effective rainfall. The IUH is derived by the Nash model for each event. The SCD is assumed to be an exponential function for each event and its parameters were correlated with the effective rainfall characteristics. A sediment routing function, based on travel time and sediment particle size, is used to predict the SCD.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.5
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• pp.1129-1136
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• 1994

A point heavy rainfall process is physically modeled. It uses meteorological variables at the ground level as its inputs. The components of the model are parameterized based on well established observations and the previous studies of cloud physics. Particular emphasis is placed on the efficiency of accretion. So we adopt the modified skew-symmetric model for hydrometeor size distribution function that is suitable for the heavy rain cloud. The dominant parameters included in the model are estimated by the optimization technique. The rainfall intensity is predicted by the model with the medium values of estimated parameters.

• Journal of Korean Society on Water Environment
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• v.21 no.6
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• pp.617-623
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• 2005

Forestry and agricultural land uses constitute 85% of Korea and these land uses are typically mixed in many watersheds. Biological Oxygen Demand (BOD) concentration is a primary factor for managing water qualities of the water resources in Korea. BOD loadings from diffuse sources, however, not well monitored yet. This study aims to assess BOD loadings from diffuse sources and their affecting factors to conserve quality of water resources. Event Mean Concentration (EMC) of BOD was calculated based on the monitoring data of forty rainfall events at four agricultural-forestry watersheds. Exceedence cumulative probability of BOD EMCs were plotted to show agricultural activities in a watershed impacts on the magnitude of EMCs. Prediction equation for each rainfall event was proposed to estimate BOD EMCs: $EMC_{BOD}(mg/L)=EXP(0.413+0.0000001157{\times}$(discharged runoff volume in $m^3$)+0.018${\times}$(ratio of agricultural land use to total watershed area).

• Water for future
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• v.24 no.1
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• pp.93-97
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• 1991

To design the minor structures in the small watersheds, it is required to calculate the peak discharge. For these calculations the simple peak flow prediction equations, the unit hydrograph method, the synthetic unit hydrograph methods or the runoff simulation models are adopted. To use these methods it is generally required to know the amount and the distributions, which are the uniform distribution, the triangular distribution, the trapezoidal distribution, or the Huff type distribution, of the design rainfall. In this study, the peak discharges are calculated by the different rainfall distribution and the values are compared.

• Journal of Environmental Science International
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• v.2 no.1
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• pp.29-36
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• 1993

An instantaneous unit sediment graph (IUSG) model is investigated for prediction of sediment yield from an upland watershed In Northwestern Mississippi. Sediment yields are predicted by convolving source runoff with an IUSG. The IUSG is the distribution of sediment from an instantaneous burst of rainfall producing one unit of runoff. The IUSG, defined as a product of the sediment concentration distribution (SCD) and the instantaneous unit hydrograph (IUH), is known to depend on the characteristics of the effective rainfall. The IUH is derived by the Nash model for each event. The SCD is assumed to be an exponential function for each event and its parameters were correlated with the effective rainfall characteristics. A sediment routing function, based on travel time and sediment particle size, is used to predict the SCD.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.13-15
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• 2014

At frequencies above 10 GHz, rain is a dominant propagation phenomenon on satellite link attenuation. The prediction of rain attenuation is based on the point rainfall rate for 0.01 % of an average year with one minute integration time. Most of available rain data have been measured with 60 minutes integration time, and many researchers have been studying on converting the rainfall rate data from various integration times to one minute integration time. This paper proposes a new Multiple Regression model for the conversion, and the proposed schemes show better performance than the existing schemes.

• Water for future
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• v.29 no.4
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• pp.109-118
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• 1996

The neural network is a mathematical model of theorized brain activity which attempts to exploit the parallel local processing and distributed storage properties. The neural metwork is a good model to be applied for the classification problem, large combinatorial optimization and nonlinear mapping. A multi-layer neural network is constructed to predict rainfall. The network learns continuourvalued input and output data. Application of neural network to 1-hour real data in Seoul metropolitan area and the Soyang River basin shows slightly good predictions. Therefore, when good data is available, the neural network is expected to predict the complicated rainfall successfully.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.5
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• pp.97-109
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• 2003

This study is mainly conducted to derive the design drought rainfall by the consecutive duration using probability weighted moments with rainfall in the regional drought frequency analysis. It is anticipated to suggest optimal design drought rainfall of hydraulic structures for the water requirement and drought frequency of occurrence for the safety of water utilization through this study. Preferentially, this study was conducted to derive the optimal regionalization of the precipitation data that can be classified by the climatologically and geographically homogeneous regions all over the regions except Cheju and Ulreung islands in Korea. Five homogeneous regions in view of topographical and climatological aspects were accomplished by K-means clustering method. Using the L-moment ratio diagram and Kolmogorov-Smirnov test, generalized extreme value distribution was confirmed as the best fitting one among applied distributions. At-site and regional parameters of the generalized extreme value distribution were estimated by the method of L-moments. Design drought rainfalls using L-moments following the consecutive duration were derived by the at-site and regional analysis using the observed and simulated data resulted from Monte Carlo techniques. Relative root-mean-square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE for the design drought rainfall derived by at-site and regional analysis in the observed an simulated data were computed and compared. In has shown that the regional frequency analysis procedure can substantially more reduce the RRMSE. RBIAS and RR in RRMSE than those of at-site analysis in the prediction of design drought rainfall. Consequently, optimal design drought rainfalls following the regions and consecutive durations were derived by the regional frequency analysis.