심층신경망(DNN) 기법을 이용한 월파량 산정

• 영문명: Estimation of Overtopping Discharges with Deep Neural Network(DNN) Method
• 발행기관: 한국연안방재학회
• 저자명: 김창겸(Changkyum Kim) 전인식(Insik Chun) 오병철(Byungcheol Oh)
• 간행물 정보: 『한국연안방재학회지』제8권 제4호, 229~236쪽, 전체 8쪽
• 주제분류: 사회과학 > 사회과학일반
• 파일형태: PDF
• 발행일자: 2021.10.31

영문 초록

An Artificial Intelligence(AI) study was conducted to calculate overtopping discharges for various coastal structures. The Deep Neural Network(DNN), one of the artificial intelligence methods, was employed in the study. The neural network was trained, validated and tested using the EurOtop database containing the experimental data collected from all over the world. To improve the accuracy of the deep neural network results, all data were non-dimensionalized and max-min normalized as a preprocessing process.  regularization was also introduced in the cost function to secure the convergence of iterative learning, and the cost function was optimized using RMSProp and Adam techniques. In order to compare the performance of DNN, additional calculations based on the multiple linear regression model and EurOtop’s overtopping formulas were done as well, using the data sets which were not included in the network training. The results showed that the predictive performance of the AI technique was relatively superior to the two other methods.

목차

1. 서론

2. 신경망 학습데이터

3. 심층신경망 기반 월파량 추정

4. 결론

키워드

Overtopping discharge Deep Neural Network Multilinear regression EurOtop database