• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.597-603
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• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.

• Journal of Korea Water Resources Association
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• v.48 no.4
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• pp.245-255
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• 2015

This study used high density network of integrated meteorological sensor, which are operated by SK Planet, with KMA weather stations to estimate the quantitative precipitation field in Seoul area. We introduced SK Planet network and analyzed quality of the observed data for 3 months data from 1 July to 30 September 2013. As the quality analysis result, we checked most SK Planet stations observed similar with previous KMA stations. We developed the real-time quality check and adjustment method to reduce the error effect for hydrological application by missing and outlier value and we confirmed the developed method can be corrected the missing and outlier value. Through this method, we used the 190 stations(KMA 34 stations, SK Planet 156 stations) that missing ratio is less than 20% and the effect of the outlier was the smallest for quantitative precipitation estimation. Moreover, we evaluated reproducibility of rainfall field high density rain gauge network has $3km^2$/gauge. As the result, the spatial relative frequency of rainfall field using SK Planet and KMA stations is similar with radar rainfall field. And, it supplement the blank of KMA observation network. Especially, through this research we will take advantage of the density of the network to estimate rainfall field which can be considered as a very good approximation of the true value.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1285-1294
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• 2021

In recent years, as rainfall is concentrated and rainfall intensity increases worldwide due to climate change, the scale of flood damage is increasing. Rainfall of a previously unobserved magnitude falls, and the rainy season lasts for a long time on record. In particular, these damages are concentrated in ASEAN countries, and at least 20 million people among ASEAN countries are affected by frequent flooding due to recent sea level rise, typhoons and torrential rain. Korea supports the domestic flood warning system to ASEAN countries through various ODA projects, but the communication network is unstable, so there is a limit to the central control method alone. Therefore, in this study, an artificial intelligence-based flood prediction model was developed to develop an observation station that can observe water level and rainfall, and even predict and warn floods at once at one observation station. Training, validation and testing were carried out for 0.5, 1, 2, 3, and 6 hours of lead time using the rainfall and water level observation data in 10-minute units from 2009 to 2020 at Junjukbi-bridge station of Seolma stream. LSTM was applied to artificial intelligence algorithm. As a result of the study, it showed excellent results in model fit and error for all lead time. In the case of a short arrival time due to a small watershed and a large watershed slope such as Seolma stream, a lead time of 1 hour will show very good prediction results. In addition, it is expected that a longer lead time is possible depending on the size and slope of the watershed.

• Journal of Wetlands Research
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• v.16 no.1
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• pp.113-124
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• 2014

The observed rainfall may be different along with the altitude of rain gauge, resulting in the fact that the characteristics of rainfall events occurred in urban or mountainous areas are different. Due to the mountainous effects, in higher altitude, the uncertainty involved in the rainfall observation gets higher so that the density of rain gauges should be more dense. Basically, a methodology for the rain gauge network evaluation, considering this altitude effect of rain gauges can account for the mountainous effects and becomes an important step for forecasting flash flood and calibrating of the radar rainfall. For this reason, in this study, we suggest a methodology for rain gauge network evaluation with consideration of the rain gauge's altitude. To explore the density of rain gauges at each level of altitude, the Equal-Altitude-Ratio of the density of rain gauges, which is based on the fixed amount of elevation and the Equal-Area-Ratio of the density of rain gauges, which is based on the fixed amount of basin area are designed. After these two methods are applied to a real watershed, it is found that the Equal-Area-Ratio generates better results for evaluation of a rain gauge network with consideration of rain gauge's altitude than the Equal-Altitude-Ratio does. In addition, for comparison between the soundness of rain gauge networks in other watersheds, the Coefficient of Variation (CV) of the rain gauge density by the Equal-Area-Ratio is served as the index for the evenness of the distribution of the rain gauge's altitude. The suggested method is applied to the five large watersheds in Korea and it is found that rain gauges installed in a watershed having less value of the CV shows more evenly distributed than the ones in a watershed having higher value of the CV.

• Atmosphere
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• v.18 no.4
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• pp.299-316
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• 2008

The intensive upper-air observation network was organized over southwestern region of the Korean Peninsula during the Korea Enhanced Observing Program in 2005 (KEOP-2005). In order to examine the effect of additional upper-air observation on the numerical weather forecasting, three Observing System Experiments (OSEs) using Korea Local Analysis and Prediction System (KLAPS) and Weather Research and Forecasting (WRF) model with KEOP-2005 data are conducted. Cold start case with KEOP-2005 data presents a remarkable predictability difference with only conventional observation data in the downstream and along the Changma front area. The sensitivity of the predictability tends to decrease under the stable atmosphere. Our results indicates that the effect of intensive observation plays a role in the forecasting of the sensitive area in the numerical model, especially under the unstable atmospheric conditions. When the intensive upper-air observation data (KEOP-2005 data) are included in the OSEs, the predictability of precipitation is partially improved. Especially, when KEOP-2005 data are assimilated at 6-hour interval, the predictability on the heavy rainfall showing higher Critical Success Index (CSI) is highly improved. Therefore it is found that KEOP-2005 data play an important role in improving the position and intensity of the simulated precipitation system.

• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.3
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• pp.4184-4194
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• 1976

Located in the southwestern part of Korea, the Yong San Gang river flows generally northeast to southwest, and because of the specific location, topography and climate, the basin area is subject to recurrent drought and flood damages. To eliminate the cause of such damages and ensure an increase in the farm income by means of effective irrigation supply and increased cropping intensity, efforts are being made to speed up implementation of an integrated agricultural development project which would include construction. of an estuary dam and irrigation facilities as well as land development and tidal reclarnation. In formulating a basin development project plan, it is necessary to study a series of long-term runoff data. The catchment area at the proposed estuary damsite is 3,471$\textrm{km}^2$ with the total length of the river channel up to this point reaching 138km. An analysis of runoff in this area was carried out. Rainfall was estimated by the Thiessen Network based on records available from 15 of the rainfall observation stations within the area. Out of the 15 stations, Kwang Ju and Mok Po stations were keeping long-term precipitation records exceeding some 60 years while the others were in possession of only 5-10 years records. The long-term records kept by those stations located in the center of the basin were used as base records and records kept by the remaining stations were supplemented using the coefficient of correlation between the records kept by the base stations and the remainder. The analyses indicate that the average annual rainfall measured at Kwang Ju during 1940-1972 (33 years) amounts to 1,262mm and the areal rainfall amounts to 1,236mm. For the purpose of runoff analysis, 7 observatories, were set up in the middle and lower reaches of the river and periodic measurements made by these stations permitted analysis of water levels and river flows. In particular, the long-term data available from Na Ju station significantly contributed to the analysis. The analysis, made by 4-stage Tank method, shows that the average annual runoff during 1940-1972 amounts to 2,189 million ㎥ at the runoff rate of 51%. As for the amount of monthly runoff, the maximum is 484.2 million ㎥ in July while the minimum is 48.3 million ㎥ in January.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.117-127
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• 2020

The Early Warning System for agrometeorological hazard of the Rural Development Administration (Korea) forecasts detailed weather for each farm based on the meteorological information provided by the Korea Meteorological Administration, and estimates the growth of crops and predicts a meteorological hazard that can occur during the growing period by using the estimated detailed meteorological information. For verification of early warning system, automated weather observation network was constructed in the study area. Moreover, a real-time web display system was built to deliver near real-time weather data collected from the observation network. The meteorological observation system collected diverse meteorological variables including temperature, humidity, solar radiation, rainfall, soil moisture, sunshine duration, wind velocity, and wind direction. These elements were collected every minute and transmitted to the server every ten minutes. The data display system is composed of three phases: the first phase builds a database of meteorological data collected from the meteorological observation system every minute; the second phase statistically analyzes the collected meteorological data at ten-minutes, one-hour, or one-day time step; and the third phase displays the collected and analyzed meteorological data on the web. The meteorological data collected in the database can be inquired through the webpage for all data points or one data point in the unit of one minute, ten minutes, one hour, or one day. Moreover, the data can be downloaded in CSV format.

• 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.

• Journal of Korea Water Resources Association
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• v.42 no.6
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• pp.493-503
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• 2009

Estimation of the mean-field bias of radar rainfall is to determine the difference between the areal means of radar and rain gauge rainfall, where the rain gauge rainfall is assumed to be the truth. To exactly determine this bias, the variance of the difference between two observations must be small enough, thus, enough number of observations is indispensible. So, the problem becomes to determine the number of rain gauges to satisfy the level of variance of the difference between two observations. Especially, this study focuses on the case when the rain gauges are disproportionate spatially. This is the problem for the Ganghwa rain radar for the observation of rainfall within the Imjin river basin and the same problem also occurs when a radar is located in between land and ocean. This study considered the Imjin river basin, and compared two cases when rain gauges are available only within the downstream part, about one third of the whole basin, and over the whole basin. Based on the results derived, the rain gauge density within the downstream part of the Imjin river basin was proposed to secure the same accuracy obtained when the rain gauges are available over the whole Imjin river basin.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.45-52
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• 2018

This study identifies the cause of the accident and presents a new concept for safe urban stream management by numerical simulating the flood event of Cheonggyecheon on August 17, 2017, using rain data measured through a dense weather observation network. In order to simulate water retention in the CSO channel listed as one of the causes of the accident, a reliable urban runoff model(XP-SWMM) was used which can simulate various channel conditions. Rainfall data measured through SK Techx using SK Telecom's cell phone station was used as rain data to simulate the event. The results of numerical simulations show that rainfall measured through AWSs of Korea Meteorological Administration did not cause an accident, but a similar accident occurred under conditions of rainfall measured in SK Techx, which could be estimated more similar to actual phenomena due to high spatial density. This means that the low spatial density rainfall data of AWSs cannot predict the actual phenomenon occurring in Cheonggyecheon and safe river management needs high spatial density weather stations. Also, the results of numerical simulation show that the residual water in the CSO channel directly contributed to the accident.