• International Journal of Contents
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• v.13 no.4
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• pp.23-28
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• 2017

Of the total economic loss caused by disasters, 40% are due to floods and floods have a severe impact on human health and life. So, it is important to monitor the water level of a river and to issue a flood warning during unfavorable circumstances. In this paper, we propose a modified error function to improve a hydrological modeling using a multi-layer perceptron (MLP) neural network. When MLP's are trained to minimize the conventional mean-squared error function, the prediction performance is poor because MLP's are highly tunned to training data. Our goal is achieved by preventing overspecialization to training data, which is the main reason for performance degradation for rare or test data. Based on the modified error function, an MLP is trained to predict the water level with rainfall data at upper reaches. Through simulations to predict the water level of Nakdong River near a UNESCO World Heritage Site "Hahoe Village," we verified that the prediction performance of MLP with the modified error function is superior to that with the conventional mean-squared error function, especially maximum error of 40.85cm vs. 55.51cm.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.188-188
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• 2017

Rainfall is an important input to hydrological models. The accuracy of hydrological studies for water resources and floods management depend primarily on the estimation of rainfall. Thailand is among the countries that have regularly affected by floods. Flood forecasting and warning are necessary to prevent or mitigate loss and damage. Merging near real time satellite-based precipitation estimation with relatively high spatial and temporal resolutions to ground gauged precipitation data could contribute to reducing uncertainty and increasing efficiency for flood forecasting application. This study tested the applicability of satellite-based rainfall for water resources management and flood forecasting. The objectives of the study are to assess uncertainty associated with satellite-based rainfall estimation, to perform bias correction for satellite-based rainfall products, and to evaluate the performance of the bias-corrected rainfall data for the prediction of flood events. This study was conducted using a case study of Thai catchments including the Chao Phraya, northeastern (Chi and Mun catchments), and the eastern catchments for the period of 2006-2015. Data used in the study included daily rainfall from ground gauges, telegauges, and near real time satellite-based rainfall products from TRMM, GSMaP and PERSIANN CCS. Uncertainty in satellite-based precipitation estimation was assessed using a set of indicators describing the capability to detect rainfall event and efficiency to capture rainfall pattern and amount. The results suggested that TRMM, GSMaP and PERSIANN CCS are potentially able to improve flood forecast especially after the process of bias correction. Recommendations for further study include extending the scope of the study from regional to national level, testing the model at finer spatial and temporal resolutions and assessing other bias correction methods.

• 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.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.116-124
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• 2023

As the flood damage on small streams increase due to the increase in frequency of extreme climate events, the need to measure hydraulic data of them has increased for disaster risk management. National Disaster Management Institute, Ministry of Interior and Safety develops CADMT, a CCTV-based automatic discharge measurement technology, and operates pilot small streams to verify its performance and develop disaster risk management technology. The research selects two small streams such as the Neungmac and the Jungsunpil streams to develop the Nomograph by using the 4-Parameter Logistic method using only the observed rainfall data from the Automatic Weather System operated by the Korea Meteorological Agency closest to the small streams and discharge data collected by using the CADMT. To evaluate developed Nomograph, the research forecasts floods discharges in each small stream and compares the result with the observed discharges. As a result of the evaluations, the forecasted value is found to represent the observed value well, so if more accurate observed data are collected and the Nomograph based on it is developed in the future, the high-accuracy flood prediction and warning will be possible.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.463-464
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• 2016

본 연구에서는 하나의 도시유역 중소하천에 대하여 수리 수문 분석을 바탕으로 홍수예경보 발령을 위한 일련의 분석 체계를 수립하였다. 이를 통해 산정된 결과물은 강우지속기간별 경보발령 기준 강우량 수치이다. 이것은 해당 하천이 합류하는 본류 하천의 배수위 영향에 따라 달라질 수 있다. 경보발령 기준 강우량 산정을 위한 도시유역 중소하천의 수리 수문 분석은 가장 일반적인 해석 모형인 HEC-HMS 및 HEC-RAS 모형들을 상호 연계하여 이루어졌으며, 경보발령 기준 강우량 산정에 있어서는 상 하류의 다양한 관측수위 조건을 고려하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.365-370
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• 2011

매년 반복되는 산간계곡에서의 인명피해를 저감하기 위하여 방재연구소에서는 "산지 돌발홍수 예측시스템"을 개발하였으며, '10년 우기철동안 시범지역의 모니터링을 통해 시스템의 문제점을 분석하고, 이를 개선하기 위한 방안을 검토하였다. 산지지역 강우에 대한 모니터링은 현재 지방자치단체에서 운영하고 있는 자동우량경보시설을 활용하였으며, 해당 시설중 특히 수위계가 설치된 지역에 대하여 검토하였다. 수위자료 모니터링 결과를 바탕으로 정확도를 검토한 결과, 5개 시범지역의 경우 약 56%의 정확도가 있는 것으로 나타났다. 산정되었다. 그러나 시스템을 수정 보완한 후에는 정확도가 66%로 증가하였다. 향후 지속적인 모니터링과 문제점 분석을 통해 정확도를 지속적으로 향상시킬 계획이다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1413-1424
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• 2013

Recently, the frequency of severe storms increases in Korea. Severe storms occurring in a short time cause huge losses of both life and property. A considerable research has been performed for the flood control system development based on an accurate stream discharge prediction. A physical model is mainly used for flood forecasting and warning. Physical rainfall-runoff models used for the conventional flood forecasting process require extensive information and data, and include uncertainties which can possibly accumulate errors during modelling processes. ANFIS, a data driven model combining neural network and fuzzy technique, can decrease the amount of physical data required for the construction of a conventional physical models and easily construct and evaluate a flood forecasting model by utilizing only rainfall and water level data. A data driven model, however, has a disadvantage that it does not provide the mathematical and physical correlations between input and output data of the model. The characteristics of a data driven model according to functional options and input data such as the change of clustering radius and training data length used in the ANFIS model were analyzed in this study. In addition, the applicability of ANFIS was evaluated through comparison with the results of HEC-HMS which is widely used for rainfall-runoff model in Korea. The neuro-fuzzy technique was applied to a Cheongmicheon Basin in the South Han River using the observed precipitation and stream level data from 2007 to 2011.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.247-262
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• 2000

In this paper, the WSANN(Water Stage Analysis with Neural Network) model was presented so as to predict flood water stage at Jindong which has been the major stream gauging station in Nakdong river basin. The WSANN model used the improved backpropagation training algorithm which was complemented by the momentum method, improvement of initial condition and adaptive-learning rate and the data which were used for this study were classified into training and testing data sets. An empirical equation was derived to determine optimal hidden layer node between the hidden layer node and threshold iteration number. And, the calibration of the WSANN model was performed by the four training data sets. As a result of calibration, the WSANN22 and WSANN32 model were selected for the optimal models which would be used for model verification. The model verification was carried out so as to evaluate model fitness with the two-untrained testing data sets. And, flood water stages were reasonably predicted through the results of statistical analysis. As results of this study, further research activities are needed for the construction of a real-time warning of the impending flood and for the control of flood water stage with neural network method in river basin. basin.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.297-297
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• 2021

전 지구적 기후변화에 따른 돌발성 집중호우의 증가와 급격한 도시화로 인해 도시홍수의 위험성이 증가하고 있으며 침수로 인한 인명 및 재산피해가 급증하고 있다. 특히 중·소규모 도시하천은 대부분 홍수유출 도달시간이 매우 짧고 수위가 급격히 상승하는 특성이 있어 집중호우에 매우 취약하여 더욱 선제적이고 정확한 홍수 예·경보가 요구된다. 현재 중·소규모 도시하천의 경우 실시간 하천수위가 위험수위에 도달할 시 하천을 통제하고 위험경보를 발령하는 방식으로 대응하고 있지만, 둔치 침수에 이르는 시간이 매우 짧아 통제가 어려우며 이마저도 행정 자치구별로 예경보 운영 및 통제기준이 상이하여 실질적인 대응에 한계가 있다. 따라서 본 연구는 강우 발생 시부터 즉각적 대응을 위해 강우-수위 관계 및 상류 돌발홍수와 강우강도의 영향을 분석하여 구간별 둔치 범람 기준을 제시하고 자치구별로 상이한 예경보 수위 기준을 통합하여 산정하는 방법을 개발하였다. 본 연구는 서울시의 주요 침수취약지구인 도림천을 대상으로 수행하였으며, 도시하천 특성의 내외수를 연계한 강우-유출모형(SWMM)을 이용하여 확률강우 시나리오에 따른 대상하천의 수위변화 및 도달시간을 분석하였다. 본 연구의 성과는 중, 소규모 도시하천에의 홍수예경보시스템 구축 및 효율적인 하천방재계획 수립에 기여할 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.816-820
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• 2005

본 연구에서는 해안 도시 하천의 범람으로 인한 홍수 재해 발생시 예상될 수 있는 피해에 대해 적절한 홍수예경보 및 피난대책을 수립하고자 대표적인 해안 도시 하천의 특성을 가지는 부산시 온천천 유역을 대상으로 수치지도에서 각종 지형자료를 추출하였고 수문 GIS 자료를 구축하였다. 그리고, 하천 수리 분석을 위한 한계유출량 산정을 위해 HEC-RAS 모형을 이용 조위의 영향을 고려하여 홍수위 및 한계유출량을 산정하였고 수문 분석을 위한 도시 돌발 홍수 기준 우량 산정을 위해 PCSWMM 2002를 이용하여 기준 우량을 산정하였다. 전형적인 해안 도시 지역 유역 특성을 나타내는 부산시 온천천 유역에 대한 경보발령 기준을 설정하기 위하여 선정지점 세 곳의 한계수심 $H_{c1},\;H_{c2},\;H_{c3},\;H_{c4}$가 발생할 수 있는 강우량(위험 홍수량을 유발하는 위험 강우량(Trigger Rainfall))을 산정하였고 PCSWMM을 이용한 모형화 기법으로 해안 도시 돌발 홍수 기준 우량을 산정하였다. 산정 결과 온천천 유역의 홍수예경보 시스템과 이에 따른 홍수예경보 발령흐름도, 운영체계가 결정되어 해안 도시 돌발 홍수예경보 방안이 구축되었다. 해안 도시의 홍수 관리는 도시 우수 시스템, 하천, 해안 특성이 복합된 문제이다. 현재 해안 도시 지역의 홍수예경보 시스템 구축 실적이 전무한 실정임을 볼 때 현실적으로 실용화 할 수 있는 시스템 개발을 해내는 것이 무엇보다도 시급하고 중요한 문제이다. 앞으로 더욱 심도있게 연구하여 주요 하천에 대한 홍수예경보 시스템 구축이 절실히 요구된다.