시계열자료의 계층분리기법을 이용한 하천유역의 홍수위 예측

• 영문명: Flood Stage Forecasting using Class Segregation Method of Time Series Data
• 발행기관: 한국방재학회
• 저자명: 김성원 Kim, Sung-Weon
• 간행물 정보: 『3. 한국방재학회 학술대회논문집』2008년, 669~674쪽, 전체 6쪽
• 주제분류: 공학 > 기타공학
• 파일형태: PDF
• 발행일자: 2008.02.28

영문 초록

In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.

키워드

Flood stage forecasting Unsupervised algorithm Kohonen self-organizing map Neural networks model