• 한국농공학회논문집
• /
• 제66권4호
• /
• pp.51-57
• /
• 2024

This study was conducted on how to simulate runoff, which was done using existing physical models, using an LSTM (Long Short-Term Memory) model based on deep learning. Tancheon, the first tributary of the Han River, was selected as the target area for the model application. To apply the model, one water level observatory and four rainfall observatories were selected, and hourly data from 2020 to 2023 were collected to apply the model. River water level of the outlet of the Tancheon basin was simulated by inputting precipitation data from four rainfall observation stations in the basin and average preceding 72-hour precipitation data for each hour. As a result of water level simulation using 2021 to 2023 data for learning and testing with 2020 data, it was confirmed that reliable simulation results were produced through appropriate learning steps, reaching a certain mean absolute error in a short period time. Despite the short data period, it was found that the mean absolute percentage error was 0.5544~0.6226%, showing an accuracy of over 99.4%. As a result of comparing the simulated and observed values of the rapidly changing river water level during a specific heavy rain period, the coefficient of determination was found to be 0.9754 and 0.9884. It was determined that the performance of LSTM, which aims to simulate river water levels, could be improved by including preceding precipitation in the input data and using precipitation data from various rainfall observation stations within the basin.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2023년도 학술발표회
• /
• pp.31-31
• /
• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

• 한국환경과학회지
• /
• 제10권5호
• /
• pp.323-327
• /
• 2001

A new method is developed to estimate the evaporation of water from a surface with high accuracy and resolution. The principle of new method is to detect a weight change of buoyant weight according to a change in water level of Class A Pan mesured by the use of a strain-gauge load cell. Field test of evaporation recording new instrument was carried out at Suwon for 10 days July 1999. It is possible in field observation to measure hourly evaporation amount by newly developed evaporation recording instrument in Class A Pan against strong solar radiation. Present study provide a possibility of domestic high accuracy instrument development below than 0.1mm water level measurement accuracy. If there is low humidity and high wind speed conditions which is possible to evaporate from water surface during night time. And it needs continuous study to understand between meteorological elements and latent heat effect at ground level by field observation study using high accuracy evaporation recording instrument.

• 한국농공학회지
• /
• 제42권
• /
• pp.24-30
• /
• 2000

A system for evaluating streamflow data (KORSAS) was developed, and is operated using PC based Windows to help the hydrological observation practitioner's working in Korea Water Resources Corporation (KOWACO). This system has modules including; DB access and data management, flow measurement arranging, H-Q relation deriving, area rainfall calculating, flow calculating, and flow evaluating modules. Evaluation of observed streamflow is accomplished through the following processes. First, hourly streamflow data is calculated from water level data stored in a DB server by applying the rating relationship between water level and flow rates derived from the past flow measurements. Second, hourly areal rainfal data is calculated from point data stored in the DB server by applying Thiessen networks. Third, hydrographs are displayed on a daily, weekly, monthly, or seasonal duration basis, and are compared to hydrographs of reservoir inflow, hydrographs at water level observation stations and hydrographs derived from simulated results using models.

• 한국농림기상학회지
• /
• 제4권4호
• /
• pp.197-202
• /
• 2002

A new method is used to estimate the amount of water evaporation from Class A Pan with higher precision and accuracy. The principle of method is to detect the weight change of a buoyant sinker resulting from a change in water level of Class A Pan. A strain-gauge load cell is used to measure the weight change. Field observation of evaporation was done at Pohang Meteorological Station from June 24 to August 4, 2002. By using this new method, it is possible to measure hourly evaporation accurately even under a strong solar radiation and wind disturbance, enabling a direct comparison of evaporation with other meteorological elements. At night, under low humidity and high wind speed conditions, more evaporation was recorded than during daytime. Maximum evaporation rates observed during this period exceed 1.0 mm/hour under the sunny and windy conditions with low humidity. To understand relationships between meteorological elements and latent heat flux at ground level, we suggest intensive held experiments using high accuracy evaporation recording instruments with hourly time interval.

• 농업과학연구
• /
• 제47권4호
• /
• pp.1029-1037
• /
• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• 한국수자원학회논문집
• /
• 제48권12호
• /
• pp.1051-1064
• /
• 2015

본 논문은 선형계획모형을 이용하여 H 아리수 정수 센터의 최적 취수량 결정 방법을 연구 하였다. 현재 H 아리수 센터에서는 관리자의 경험과 숙련도에 의지하여 취수량을 결정하고 있다. 그런데 매시 변하는 수요를 만족 시키면서 시간대별로 요금이 서로 다른 전력의 사용을 최소화 하는 취수량 결정은 근무자들의 경험과 숙련도를 넘어서는 간단한 문제가 아니다. 따라서 수리적 기법 중 하나인 선형계획모형을 이용해 취수량을 결정하고, 비용 절감을 시도하였다. 본 연구에서 제안한 선형계획 모형은 수요예측치를 기본 입력자료로 사용하고 있는데 예측오차가 발생할 경우 정수지 높이 제한을 위반하는 경우가 발생한다. 이를 해결하기 위해서는 정확한 수요예측이 선행되어야 한다. 그러나 아무리 좋은 예측 기법을 사용하더라도 실수요와 오차는 있게 마련이고 이는 여전히 높이 제한의 제약을 만족 시키지 못하는 결과를 불러일으킨다. 따라서 예측오차를 수용 할 수 있는 안전 마진 상수를 이용한 대안을 제안하였다. 본 연구에서 제안한 선형 계획 모형을 통한 취수량 결정은 수위 모니터링을 위해 항시 작업자가 근무 할 필요가 없기 때문에 인건비 면에서도 많은 절약이 예측되어 총 비용 감축은 훨씬 더 많으리라 기대된다.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2018년도 학술발표회
• /
• pp.147-147
• /
• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• 한국콘텐츠학회논문지
• /
• 제16권4호
• /
• pp.604-613
• /
• 2016

지구온난화로 인한 바닷물의 열팽창, 빙하의 해빙 등으로 지구의 해수면은 매년 약 2.0mm/yr의 속도로 상승하고 있다. 그러나 해안에 인접한 하천을 설계할 시 기준이 되는 외수위는 과거 관측된 조위 값으로부터 4대 분조 및 조화상수를 분석하여 결정된다. 따라서, 외수위는 구조물의 설계빈도에 상응하는 해수면의 상승속도를 감안해야 할 필요가 있다고 사료된다. 본 연구에서는 국립해양조사원에서 운영하고 있는 47개소의 조위관측소를 대상으로 관측개시일부터 2015년까지 시단위로 조위자료를 수집하였다. 우리나라를 크게 서해, 남해, 동해, 제주 총 4개의 해역으로 구분하여 연별 변동추이 및 연평균 상승률 분석을 수행하였다. 그리고 기존 설계된 해안에 인접한 하천의 외수위를 검토하였다. 추후 국지적 해수면상승의 원인규명 및 외수위 고려 시 기초자료로 활용될 것으로 판단된다.

• 한국환경과학회지
• /
• 제18권3호
• /
• pp.309-314
• /
• 2009

A accurate reservoir inflow is very important as providing information for decision making about the water balance and the flood control, as well as for dam safety. The methods to calculate the inflow were divided by the directed method to measure streamflow from upstream reservoirs and the indirected method to estimate using the correlation of reservoir water level and release. Currently, the inflow of multi-purpose dam is being calculated by the indirect method and the reservoir water level to calculate the storage capacity is being used by centimeters(cm) units. Corresponding to the storage volume of 1cm according to scale and water level of multi-purpose dam comes up to from several 10 thousand tons to several million tons. If it converts to inflow during 1 hour, and it comes to several hundred $m^3/sec$(CMS). Therefore, the inflow calculated on the hourly is largely deviated along the water level changes and is occurred minus value as the case. In this research, the water level gage has been developed so that it can measure a accurate water level for the improvement for the error and derivation of inflow, even though there might be various hydrology and meteorologic considerations to analyse the water balance of reservoir. Also, it is confirmed that the error and the standard derivation of data observed by the new gage is decreased by 89,6% and 1/3 & 87% and 2/3 compared to that observed by the existing gage of Daecheong and Juam multi-purpose dam.