인공신경망기법에 상관계수를 고려한 서울 강우관측 지점 간의 강우보완 및 예측

• 영문명: Rainfall Adjust and Forecasting in Seoul Using a Artificial Neural Network Technique Including a Correlation Coefficient
• 발행기관: 한국방재학회
• 저자명: 안정환 정희선 박인찬 조원철 Ahn, Jeong-Whan Jung, Hee-Sun Park, In-Chan Cho, Won-Cheol
• 간행물 정보: 『3. 한국방재학회 학술대회논문집』2008년, 101~104쪽, 전체 4쪽
• 주제분류: 공학 > 기타공학
• 파일형태: PDF
• 발행일자: 2008.02.28

영문 초록

In this study, rainfall adjust and forecasting using artificial neural network(ANN) which includes a correlation coefficient is application in Seoul region. It analyzed one-hour rainfall data which has been reported in 25 region in seoul during from 2000 to 2006 at rainfall observatory by AWS. The ANN learning algorithm apply for input data that each region using cross-correlation will use the highest correlation coefficient region. In addition, rainfall adjust analyzed the minimum error based on correlation coefficient and determination coefficient related to the input region. ANN model used back-propagation algorithm for learning algorithm. In case of the back-propagation algorithm, many attempts and efforts are required to find the optimum neural network structure as applied model. This is calculated similar to the observed rainfall that the correlation coefficient was 0.98 in missing rainfall adjust at 10 region. As a result, ANN model has been for suitable for rainfall adjust. It is considered that the result will be more accurate when it includes climate data affecting rainfall.

키워드

Artificial neural network(ANN) Rainfall forecasting correlation-coefficient determination coefficient back-propagation algorithm