• Journal of The Korean Society of Agricultural Engineers
• /
• v.49 no.4
• /
• pp.13-22
• /
• 2007

The uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modeling system under uncertainty was described and demonstrated for use in watershed management and receiving-water quality prediction. A watershed model (HSPF), a receiving water quality model (WASP), and a wetland model (NPS-WET) were incorporated into an integrated modeling system (modified-BASINS) and applied to the Hwaseong Reservoir watershed. Reservoir water quality was predicted using the calibrated integrated modeling system, and the deterministic integrated modeling output was useful for estimating mean water quality given future watershed conditions and assessing the spatial distribution of pollutant loads. A Monte Carlo simulation was used to investigate the effect of various uncertainties on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorous (T-P) in the Hwaseong Reservoir, considering uncertainty, would be less than about 4.8 and 0.26 mg 4.8 and 0.26 mg $L^{-1}$, respectively, with 95% confidence. The effects of two watershed management practices, a wastewater treatment plant (WWTP) and a constructed wetland (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaseong Reservoir to less than 3.54 and 0.15 mg ${L^{-1}$, 26.7 and 42.9% improvements, respectively, with 95% confidence. Overall, the Monte Carlo simulation in the integrated modeling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on probability and level of risk, and its application is recommended.

• Journal of Forest and Environmental Science
• /
• v.30 no.1
• /
• pp.76-84
• /
• 2014

This study was performed to monitor the effects of water level change on changes of landscape, vegetation community, and species diversity of riparian forest. Hantan river dam, study area, has been constructed in the area of Chansoo-myeon, Pocheon-si and Yeoncheon-eup, Yeoncheon-gun, Gyeonggi-do, which is a dam for flood control only in flooding season. Landscape changes were notable after the construction of coffer dam, and the changes were caused by water level increase in areas of riparian forests which consisted of mainly withered willow as a dominant species in the flooding season. It changed vegetation communities of riparian forest from Phragmites japonica and Salix koreensis to Phragmites japonica. Species diversity index was lowest in 2010 when the coffer dam was constructed and showed an increasing trend later. Thus, this study is well in agreement with a previous report that plants of the genus Salix wither by muddy water during flooding and also suggests, controlling water level of river and prediction of water level change's effects should be considered when any facilities are planned.

• Proceedings of the Korea Contents Association Conference
• /
• 2019.05a
• /
• pp.3-4
• /
• 2019

강의 수위 예측은 강 유역의 홍수 발생에 대한 방재 차원에서 아주 중요하다. 이 논문에서는 낙동강을 대상으로 수위를 예측하는 신경회로망 모델을 기반으로 홍수위에 도달하는 입력 조건을 학습에 의해 찾아내는 방법을 제시한다.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.36 no.2
• /
• pp.96-110
• /
• 1994

The purpose of this study is to develop soil salt prediction model for the estimation of irrigation water requirements for dry field crops in reclaimed tidelands. The simulation model based on water balance equation, salt balance equation, and salt storage equation was developed for daily prediction of sa]t concentration in root zone. The data obtained from field measurement during the growing period of tomato were used to evaluate the applicability of this model. The results of this study are summarized as follows: 1.The optimum irrigation point which maximizes the crop yield in reclaimed tidelands of silt loam soil while maintaining the salt concentration within the tolerance level, ws found to be pF 1.6, and total irrigation requirement after transplanting was 602mm(6.7 mm/day)for tomato. 2.When the irrigation point was pF 1.6, the deviation between predicted and measured salt concentration was less than 4 % at the significance level of 1 7% 3.Since the deviations between predicted and measured values data decrease as the amount of irrigation water increases, the proposed model appear to be more suitable for use in reclaimed tidelands. 4.The amount of irrigation water estimated by the simulation model was 7.2mm/day in the average for cultivating tomato at the optimum irrigation point of pF 1.6.The simulation model proposed in this study can be generalized by applying it to other crops. This, model, also, could be further improved and extended to estimate desalinization effects in reclaimed tidelands by including meteorological effect, capillary phenomenon, and infiltration.

• The Journal of the Korea Contents Association
• /
• v.9 no.6
• /
• pp.9-16
• /
• 2009

According to the construction of infrastructure for the water resource management services, the importance of the eco-friendly reservoir tank management is being spotlighted. In this paper, we proposed the eco-friendly reservoir tank management using prediction for improving the water quality and on-line managing efforts of reservoir tanks. The proposed method defined the context and environment of the reservoir tank and predicted the profited service according to the pump motion, the solar battery, the chemicals, the water level, the telephone line, and the modem using collaborative filtering. To evaluate the performance of the eco-friendly reservoir tank management system using prediction, we conducted sample T-tests so as to verify usefulness. This evaluation found that the difference of satisfaction by service was statistically meaningful, and showed high satisfaction. Accordingly, the satisfaction and the quality of services will be improved the efficient prediction by supporting the context information as well as the environment information.

• Journal of Ocean Engineering and Technology
• /
• v.5 no.1
• /
• pp.25-34
• /
• 1991

An accurate estimation of water level variation when thewaves propagate to the coastal regionis very important for the port and harbor development plan. This study describes the application of a hybrid element model to harbor oscillation problem due to the construction of shore structure and implementation of shore boundary. The site selected is Pusan Harbor area with the third development and the Artificial Island plan. The observed water level changes at the site are compared with the result of the numerical experiment. The model gives a very important prediction of water level changes for navigation and harbor design.

• Journal of Environmental Science International
• /
• v.31 no.12
• /
• pp.1027-1038
• /
• 2022

The intertidal zone has both land and marine characteristics and shows complex weather environments. These characteristics are suited for studying climate change, energy balance and ecosystems, and may play an important role in coastal and marine weather prediction and analysis. This study was conducted at Odo Island, approximately 300m from the mainland in Yeosu. We built a weather observation system capable of real-time monitoring on the mud flat in the intertidal zone and measured actual weather and marine data. Weather observation was conducted from April to June 2022. The results showed changes in air temperature and water temperature with changes in the tide level during spring. Correlation analysis revealed characteristic changes in air temperature and water temperature during the day and night, and with inundation and exposure.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.491-493
• /
• 2022

Flood damage due to torrential rains and typhoons is occurring in many parts of the world. In this paper, we propose a water level prediction model using water level, precipitation, and humidity data, which are key parameters for flood prediction, as input data. Based on the LSTM and GRU models, which have already proven time-series data prediction performance in many research fields, different input datasets were constructed using the ASOS(Automated Synoptic Observing System) data and AWS(Automatic Weather System) data provided by the Korea Meteorological Administration, and performance comparison experiments were conducted. As a result, the best results were obtained when using ASOS data. Through this paper, a performance comparison experiment was conducted according to the input data, and as a future study, it is thought that it can be used as an initial study to develop a system that can make an evacuation decision in advance in connection with the flood risk determination model.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.62 no.6
• /
• pp.1-9
• /
• 2020

Reservoir storage and water level information is essential for accurate drought monitoring and prediction. In particular, the agricultural drought has increased the risk of agricultural water shortages due to regional bias in reservoirs and water supply facilities, which are major water supply facilities for agricultural water. Therefore, it is important to evaluate the available water capacity of the reservoir, and it is necessary to determine the water surface area and water capacity. Remote sensing provides images of temporal water storage and level variations, and a combination of both measurement techniques can indicate a change in water volume. In areas of ungauged water volume, satellite remote sensing image acts as a powerful tool to measure changes in surface water level. The purpose of this study is to estimate of reservoir storage and level variations using satellite remote sensing image combined with hydrological statistical data and the Normalized Difference Water Index (NDWI). Water surface areas were estimated using the Sentinel-2 satellite images in Seosan, Chungcheongnam-do from 2016 to 2018. The remote sensing-based reservoir storage estimation algorithm from this study is general and transferable to applications for lakes and reservoirs. The data set can be used for improving the representation of water resources management for incorporating lakes into weather forecasting models and climate models, and hydrologic processes.

• Journal of Korean Society of Water and Wastewater
• /
• v.19 no.5
• /
• pp.572-581
• /
• 2005

This research is conducted to develop predictable method of real scale nanofiltration treatability with small scale nanofiltration experiments. As a result of comparing calculated values with measured values, they are in a good agreement for the concentrations in filtered water and concentrated water. The results of that are not affected by change of system recovery from 20% to 95%. The proposed method is produced using constant recovery of elements, that is, no considering the pressure change. we can predict filtrated flux and contaminant concentrations with the method. The method has the following steps. (1) Calculate recovery of each element with water quality level after fixing recovery elements, (2) Predict system recovery with recovery of elements in 1, 2, 3, and 4 banks, (3) Run small scale nanofiltration experiments in predicted water quality and (4) Simulate large scale nanofiltration system for forecasting actual water quality. As the cost for nanofiltration pretest will reduced if we use the proposed method, it will be a promising method for introducing nanofiltration to supply safe drinking water.