• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.38-38
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• 2001

The rainfall estimates give sufficiently accurate information to map areas which have received the minimum rainfall necessary for outbreaks of pests such as locusts, thus cutting down the cost of searching for likely outbreak sites. At the other end of the scale, satellite rainfall estimates can be used to give timely warnings of changes in river levels and the likelihood of floods in large river catchments.(omitted)

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.198-203
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• 2020

In this paper, we propose an artificial water level prediction system for small river flood prediction. River level prediction can be a measure to reduce flood damage. However, it is difficult to build a flood model in river because of the inherent nature of the river or rainfall that affects river flooding. In general, the downstream water level is affected by the water level at adjacent upstream. Therefore, in this study, we constructed an artificial intelligence model using Recurrent Neural Network(LSTM) that predicts the water level of downstream with the water level of two upstream points. The proposed artificial intelligence system designed a water level meter and built a server using Nodejs. The proposed neural network hardware system can predict the water level every 6 hours in the real river.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.277-280
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• 2002

An artificial neural network model was developed to analyze and forecast real time river runoff from the Naju watershed, in Korea. Model forecasts are very accurate (i.e., relative error is less than 3% and $R^2$ is great than 0.99) for calibration data sets. Increasing the time horizon for validation data sets, thus making the model suitable for flood forecasting, decreases the accuracy of the model. The resulting optimal EBPN models for forecasting real time runoff consists of ten rainfall and four and ten runoff data (ANN0410 and ANN1010 models). Performances of the ANN0410 and ANN1010 models remain satisfactory up to 6 hours (i.e., $R^2$ is great than 0.92).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.6 no.2
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• pp.57-65
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• 1986

The need of forecasting river flows arised whenever a river authority must make controls to protect the life and property from the flood and maintain the adequate flows for water use. This study is on the real time flood forecasting from the gauged and ungauged rainfall input and identification of second-order autoregressive(AR(2)) which is used as system model. A Kalman filter is used to obtain the values of the system parameters needed for the optimal control strategy. This system model was applied to the data at the Naiu gauging station in Young san river basin to check the accuracy and efficiency of prediction. One step ahead prediction is checked by stochastic analysis and the order of autoregressive model is proved to be satisfied, Discussions on interesting features of the model are presented.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.981-992
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• 2023

Due to recent global climate change, the scale of flood damage is increasing as rainfall is concentrated and its intensity increases. Rain on a scale that has not been observed in the past may fall, and long-term rainy seasons that have not been recorded may occur. These damages are also concentrated in ASEAN countries, and many people in ASEAN countries are affected, along with frequent occurrences of flooding due to typhoons and torrential rains. In particular, the Bandung region which is located in the Upper Chitarum River basin in Indonesia has topographical characteristics in the form of a basin, making it very vulnerable to flooding. Accordingly, through the Official Development Assistance (ODA), a flood forecasting and warning system was established for the Upper Citarium River basin in 2017 and is currently in operation. Nevertheless, the Upper Citarium River basin is still exposed to the risk of human and property damage in the event of a flood, so efforts to reduce damage through fast and accurate flood forecasting are continuously needed. Therefore, in this study an artificial intelligence-based river flood water level forecasting model for Dayeu Kolot as a target station was developed by using 10-minute hydrological data from 4 rainfall stations and 1 water level station. Using 10-minute hydrological observation data from 6 stations from January 2017 to January 2021, learning, verification, and testing were performed for lead time such as 0.5, 1, 2, 3, 4, 5 and 6 hour and LSTM was applied as an artificial intelligence algorithm. As a result of the study, good results were shown in model fit and error for all lead times, and as a result of reviewing the prediction accuracy according to the learning dataset conditions, it is expected to be used to build an efficient artificial intelligence-based model as it secures prediction accuracy similar to that of using all observation stations even when there are few reference stations.

• Journal of Korea Water Resources Association
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• v.51 no.2
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• pp.165-174
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• 2018

The model using time series data can be considered as a flood forecasting model of a small river due to its efficiency for model development and the advantage of rapid simulation for securing predicted time when reliable data are obtained. Transfer Function Noise (TFN) model has been applied hourly flood forecast in Italy, and UK since 1970s, while it has mainly been used for long-term simulations in daily or monthly basis in Korea. Recently, accumulating hydrological data with good quality have made it possible to simulate hourly flood prediction. The purpose of this study is to assess the TFN model applicability that can reflect exogenous variables by combining dynamic system and error term to reduce prediction error for tributary rivers. TFN model with hourly data had better results than result from Storage Function Model (SFM), according to the flood events. And it is expected to expand to similar sized streams in the future.

• Magazine of the Korean Society of Agricultural Engineers
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• v.38 no.5
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• pp.64-73
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• 1996

When typhoon occurs, the meteorological conditions get worse and can cause a large damage from storm and flood . This damage, however, can be minimized if a precise analysis of the runoff characteristics by typhoon tracks is used in the flood contorl This paper aims at the analysis of storm occurrence and runoff characteristics by typhoon tracks in Nakdong river basin. Therefore, the data of 14 typhoons which invaded Nakdong river basin during the period from 1975 to 1991 were collected, analyzed, and studied. The major results of this study are as followings; 1) The frequency of the typhoon occurrence here in Korea was affected by the storms three times a year on the average. The highest-recorded frequency was during the months of July to September. 2) The survey of the track characteristics depending on the forms of the storm in the Nakdong river basin showed that typhoon storm advanced from the south of the basin to the north, while the frontal type storm was most likely to advanced from the west to the north. 3) Typhoon tracks are classified into three categories, 6 predictors with high correlation coefficient are finally selected, and stepwise multiple regression method are used to establish typhoon strom forecasting models. 4) The riview on the directions of progress of the storm made it clear that the storm moving downstream from upstream of the basin could develop into peak discharge for ca short time and lead to more flood damage than in any other direction.

• Water for future
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• v.18 no.2
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• pp.153-161
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• 1985

The methods for hydrologic prediction of streamflows for more efficient and functional operations and automation of the flood warning and forecasting system have been studiedand which have been widely used in the control engineering have been studied and investigated for representation of the dynamic behavior of rainfall-runoff precesses, and formulated into mathematical model form. The applicabilities of the model using the adaptive Kalman filter algorithm to the on-line, real-time prediction of river flows have been worked out. The computer programs in FORTRAN which are developed here can be utilized for more efficient operations and better prediction abilities of flood warning and forecasting systems, and also should be modified for better model performance.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.23-31
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• 2007

The objective of the study is to prepare input data for FIA (Flood Inundation Analysis) & FDA (Flood Damage Assessment) through rainfall-runoff simulation by HEC-HMS model. For HwaOng watershed (235.6 $km^{2}$), HEC-HMS was calibrated using 6 storm events. Geospatial data processors, HEC-GeoHMS is used for HEC-HMS basin input data. The parameters of rainfall loss rate and unit hydrograph are optimized from the observed data. HEC-HMS was applied to simulate rainfall-runoff relation to frequency storm at the HwaOng watershed. The results will be used for mitigating and predicting the flood damage after river routing and inundation propagation analysis through various flood scenarios.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.347-359
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• 2010

A simple web-based flood forecasting system using data from stage and rainfall monitoring stations was developed to solve the difficulty that real-time forecasting model could not get the reliabilities because of assumption of future rainfall duration and intensity. The regression model in this research could forecast future water level of maximum 2 hours after using data from stage and rainfall monitoring stations in Daejeon area. Real time stage and rainfall data were transformed from web-sites of Geum River Flood Control Office & Han River Flood Control Office based MS-Excel 2007. It showed stable forecasts by its maximum standard deviation of 5 cm, means of 1~4 cm and most of improved coefficient of determinations were over 0.95. It showed also more researches about the stationarity of watershed and time-series approach are necessary.