• Proceedings of the Korea Water Resources Association Conference
• /
• 한국수자원학회 2023년도 학술발표회
• /
• pp.309-309
• /
• 2023

자연재해대책법에 따라 방재성능목표를 달성하기 위해 행안부 및 지자체는 방재성능목표강우량을 설정·운영하고 있다. 현재, 기후변화로 인한 할증률을 산정하여 방재성능목표강우량 산정에 포함하고 있으나, 기후의 내적변동성으로 인한 강수의 변화는 반영하지 못하고 있는 실정이다. 이에 본 연구에서는 기후변화뿐만 아니라, 엘리뇨, 라니뇨, ENSO 등과 같이 지구의 내적인 원인들로 인해 변화하는 기후내적변동성(Climate Internal Variability, CIV)을 추가적으로 고려하여 할증률 개념을 확장하고자 한다. 외부의 Forcing 변화(즉 기후변화)가 없더라도 자연적으로 기후가 변동하는 현상을 모의하기 위해, 크게 3개 동역학적, 통계학적, 추계학적 방법들이 적용되어 기후내적 변동성을 정량화하고 있다. 본 연구에서는 기후에 대한 일기를 추계학적으로 오랜 기간 동안 생성하고 생성된 시계열을 바탕으로 자연적인 변동성을 추출(Stochastic Approach)하는 방법을 사용하여 기후내적변동성을 추정할 것이다. 구체적으로, 생성된 앙상블 시계열에 Detrended 방법과Differenced 방법을 각각 적용하여 기후내적변동성의 크기를 정량화하고 상호 비교할 예정이다. 정량화된 기후내적변동성의 크기는 추계학적 할증률로 변환될 것이며 방재성능목표강우량 산정에 포함시켜 과거 기왕최대강우량을 갱신하는 지역에 대한 위험도를 추가로 제시할 수 있을 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 한국수자원학회 2020년도 학술발표회
• /
• pp.95-95
• /
• 2020

Urban flooding occurs in the form of internal-water inundation on roads and lowlands due to heavy rainfall. Unlike in the case of rivers, inundation in urban areas there is lacking in research on predicting and warning through measurement data. In order to analyze urban flood patterns and prevent damage, it is necessary to analyze flooding measurement data for various rainfalls. In this study, the pattern of urban flooding caused by rainfall was analyzed by utilizing the urban flooding measuring sensor, which is being test-run in the flood prone zone for urban flooding management. For analysis, 2019 rainfall data, surface water depth data, and water level data of a street inlet (storm water pipeline) were used. The analysis showed that the amount of rainfall that causes flooding in the target area was identified, and the timing of inundation varies depending on the rainfall pattern. The results of the analysis can be used as verification data for the urban inundation limit rainfall under development. In addition, by using rainfall intensity and rainfall patterns that affect the flooding, it can be used as data for establishing rainfall criteria of urban flooding and predicting that may occur in the future.

• Proceedings of the Korea Water Resources Association Conference
• /
• 한국수자원학회 2020년도 학술발표회
• /
• pp.96-96
• /
• 2020

In order to establish the risk criteria of inundation due to typhoons or heavy rainfall, research is underway to predict the limit rainfall using basin characteristics, limit rainfall and artificial intelligence algorithms. In order to improve the model performance in estimating the limit rainfall, the learning data are used after the pre-processing. When 50.0% of the entire data was removed as an outlier in the pre-processing process, it was confirmed that the accuracy is over 90%. However, the use rate of learning data is very low, so there is a limitation that various characteristics cannot be considered. Accordingly, in order to predict the limit rainfall reflecting various watershed characteristics by increasing the use rate of learning data, the watersheds with similar characteristics were clustered. The algorithms used for clustering are K-Means, Agglomerative, DBSCAN and Spectral Clustering. The k-Means, DBSCAN and Agglomerative clustering algorithms are clustered at the impervious area ratio, and the Spectral clustering algorithm is clustered in various forms depending on the parameters. If the results of the clustering algorithm are applied to the limit rainfall prediction algorithm, various watershed characteristics will be considered, and at the same time, the performance of predicting the limit rainfall will be improved.

• Water for future
• /
• 제35권6호
• /
• pp.79-95
• /
• 2002

• Water for future
• /
• 제28권5호
• /
• pp.46-52
• /
• 1995

• Water for future
• /
• 제30권5호
• /
• pp.88-90
• /
• 1997

• Water for future
• /
• 제8권2호
• /
• pp.41-48
• /
• 1975

• Water for future
• /
• 제49권3호
• /
• pp.31-40
• /
• 2016

• Water for future
• /
• 제44권4호
• /
• pp.8-15
• /
• 2011

• Water for future
• /
• 제32권4호
• /
• pp.44-48
• /
• 1999