본 연구에서는 배수 관망 정체구역에 자동 퇴수시스템을 구축하여 실시간 관망 수질 원격 감시와 퇴수 작업의 자동화를 통해 안전한 수돗물 공급 및 선진화된 배수관 수질관리 기법을 마련하고자 하였다. 이를 위해 배급수관망 실증단지의 배급수 블록에서 수질 거동을 우선적으로 모의하고 선정된 정체 구역에 대한 현장 조사를 통해 접근성이 용이한 구간에 자동 ...퇴수시스템을 구축하여 장기 운전하였다. 자동 퇴수시스템에서 무퇴수 조건으로 정체수 수질을 측정한 결과 0.1 mg/L 미만 잔류염소농도 출현주기가 0.5 NTU 초과 탁도 출현주기보다 짧아 퇴수밸브 작동의 critical 인자는 잔류염소임을 확인하였다. 잔류염소를 목표농도까지 회복시키는데 필요한 자동퇴수시스템의 퇴수량은 기존 수동 퇴수량의 10~100%로 시간과 수온에 따라 변했고 현장 특성에 맞는 퇴수량과 퇴수 주기의 조절이 필요했다. 해당 관망 정체구역에 자동 퇴수시스템을 도입할 경우 연간 퇴수량은 65% 절감되고 퇴수 작업시간은 80%로 단축되는 효과를 보였다. 관망 자동 퇴수시스템은 원격으로 수질을 감시하면서 퇴수 후의 수질 회복 상태를 관리할 수 있고 관망에서 정체수를 신속하게 배제하여 수질을 안정적으로 확보할 수 있음을 확인하여 그 적용성이 검증되었다. 그러나 다른 다양한 관망 정체구역에 자동 퇴수시스템을 구축할 경우 본 연구의 접근법이 활용될 수 있으나 해당 적용구간의 관망 정보와 물사용 특성에 따라 퇴수 조건의 변화 등 최적화가 필요할 것으로 판단된다. This study provides a case study using an automatic draining solution within the drinking distribution system where it was difficult to maintain an acceptable level of retention time and water quality. Water quality behavior was simulated using EPANET 2.0 in a block system of the distribution network system linked with a given reservoir. The site surveys for the simulated stagnant areas were used for building the automatic drain system at the accessible site. Water quality online monitoring without drain showed that earlier occurrence event of residual chlorine below 0.1 mg/L with 45 days than that of turbidity above 0.5 NTU with 90 days was a critical factor to open and close the drain valve. The discharge volumes of the automatic drain system at the stagnant area were 10~100% of the flushing volume drained by the existing manual drain work. It was confirmed that the volume and cycle of drain could be flexibly changed over time and water temperature. The long term operation of the automatic drain system at the test bed showed that the annual flushing volume and working time could be reduced by 65% and 80%, respectively. The continuous operation of the automatic drain system showed that optimal flushing and online sensing compared to manual drainage could reduce maintenance costs and ensure stable water quality of stagnant drinking water. Although the scope and effect of this study are limited to a particular water pipe, the applicability of an automatic drain system can be evaluated by the approach of this study with optimized drain conditions. KCI Citation Count: 0
This study used an observational analysis and a numerical model to investigate the heavy rainfall event that occurred on 26-27 July 2011 over the middle Korean Peninsula. Radar observations revealed ...a significant transition in the echo pattern, with strong convective systems along the coasts of southern Hwanghae-do (HHD) and Gyeonggi-do (0600-1200 UTC 26 July, stage 1), a medium-sized SW-NE precipitation band over the middle Korean Peninsula and major convective systems in the west (1500-2200 UTC, stage 2), and a narrow convective band from Gyeonggi Bay to the Seoul metropolitan area (2200 UTC 26-0100 UTC 27 July, stage 3). This study focused on the development of heavy precipitation systems during stages 1 and 2. A mesoscale ridge and trough developed over the middle peninsula at approximately 0600 UTC on 26 July and persisted throughout the first 2 stages. Both the observation and the numerical simulation suggest that a cold pool, which induced the mesoscale ridge, developed over the inland HHD in response to evaporative cooling of rain water. The outflow associated with the cold pool was found to trigger the strong convective systems along the southern coast of HHD in stage 1. The lifting of air in the precipitation band of stage 2 was mainly caused by convergence ahead of the strong low-level southwesterly flow. The numerical simulations indicated that the terrain over the Korean Peninsula contributed to the enhancement of the heavy rainfall primarily through its blocking effects on oncoming southwesterly airflow at low levels.
Recently, with the development of data processing technology and the increase of computational power, methods to solving social problems using Artificial Intelligence (AI) are in the spotlight, and ...AI technologies are replacing and supplementing existing traditional methods in various fields. Meanwhile in Korea, heavy rain is one of the representative factors of natural disasters that cause enormous economic damage and casualties every year. Accurate prediction of heavy rainfall over the Korean peninsula is very difficult due to its geographical features, located between the Eurasian continent and the Pacific Ocean at mid-latitude, and the influence of the summer monsoon. In order to deal with such problems, the Korea Meteorological Administration operates various state-of-the-art observation equipment and a newly developed global atmospheric model system. Nevertheless, for precipitation nowcasting, the use of a separate system based on the extrapolation method is required due to the intrinsic characteristics associated with the operation of numerical weather prediction models. The predictability of existing precipitation nowcasting is reliable in the early stage of forecasting but decreases sharply as forecast lead time increases. At this point, AI technologies to deal with spatio-temporal features of data are expected to greatly contribute to overcoming the limitations of existing precipitation nowcasting systems. Thus, in this project the dataset required to develop, train, and verify deep learning-based precipitation nowcasting models has been constructed in a regularized form. The dataset not only provides various variables obtained from multiple sources, but also coincides with each other in spatio-temporal specifications.
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