인공신경망 앙상블 모델 기반 동중국해 북부해역의 3차원 수온장 추정
Estimation of Three-Dimensional Temperature in the Northern East China Sea Using an Ensemble Model Based on Artificial Neural Networks
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
This study used an artificial neural network model to predict the three-dimensional temperature field by capturing the non-linear relationships between input and target data. Unlike most previous studies, which focused on open ocean regions, this research develops a model specifically designed to address the unique characteristics of the coastal areas in the Northern East China Sea. Vertical temperature profiles observed from 2000 to 2022 were used as target data, while input data representing temperature structures or forcing temperature variations were utilized for training the artificial neural network model. The optimized artificial neural network model achieved a root mean square error of 1.1°C on the test dataset. Therefore, the artificial neural network model established in this study is expected to be effectively applied to predict the three-dimensional temperature fields in the coastal seas.
Abstract
This study used an artificial neural network model to predict the three-dimensional temperature field by capturing the non-linear relationships between input and target data. Unlike most previous studies, which focused on open ocean regions, this research develops a model specifically designed to address the unique characteristics of the coastal areas in the Northern East China Sea. Vertical temperature profiles observed from 2000 to 2022 were used as target data, while input data representing temperature structures or forcing temperature variations were utilized for training the artificial neural network model. The optimized artificial neural network model achieved a root mean square error of 1.1°C on the test dataset. Therefore, the artificial neural network model established in this study is expected to be effectively applied to predict the three-dimensional temperature fields in the coastal seas.
참고문헌 (22)
- 국립해양조사원, "우리바다 해양정보도", 국립해양조사원, 126~ (2022)
- 김성중, "지난 130년 간 한반도 근해의 표층 수온 변화 경향", Ocean Polar Res, 33(3), pp.281~290 (2011)[DOI]
- 김주일, "한국 남해 연안 전선 구조에 따른 멸치 알의 분포", 한국어류학회지, 17(3), pp.205~216 (2005)
- 나정열, "한반도 근해의 해류와 해수 특성-남해연안수 확장과 수온변화-", Korean J Fish Aquat Sci, 23(4), pp.267~279 (1990)
- 민홍식, "한국 연안 표층수온의 경년변동과 장기변화", Ocean Polar Res, 28(4), pp.415~423 (2006)[DOI]
- 승영호, "한반도 주변의 수괴와 해수순환", 한국해양학회지 바다, 27(4), pp.324~331 (1992)
- 윤동영, "제주 남부해역 수온 수직구조의 공간분포 특성 파악", 한국지리정보학회지, 15(4), pp.162~174 (2012)[DOI]
- 이은주, "비주석 재귀신경망 앙상블 모델을 기반으로 한 조위관측소 해수위의준실시간 이상값 탐지", 한국해양학회지 바다, 26(4), pp.307~326 (2021)
- 주호정, "한국 남부 해역 SST의 계절 및 경년 변동이 단기 딥러닝 모델의 SST 예측에 미치는 영향", 한국해양학회지 바다, 27(2), pp.49~70 (2022)
- Aparna SG, "Prediction of daily sea surface temperature using artificial neural networks", Int J Remote Sens, 39(12), pp.4214~4231 (2018)[DOI]
- Bolton T, "Applications of deep learning to ocean data inference and subgrid parameterization", J Adv Model Earth Syst, 11, pp.376~399 (2019)[DOI]
- Chae JY, "Prediction of sea surface current around the Korean peninsula using artificial neural networks", J Geophys Res : Machine Learning and Computation, 1(4), pp.e2024JH000168~ (2024)[DOI]
- Kwon K, "Assessment and improvement of global gridded sea surface temperature datasets in the Yellow Sea using in situ ocean buoy and research vessel observations", Remote Sens, 12(5), pp.759~ (2020)[DOI]
- Lahoz WA, "Data assimilation : making sense of Earth Observation", Front Environ Sci, 2, pp.16~ (2014)[DOI]
- Lee EJ, "Reconstruction of long-term sea-level data gaps of tide gauge records using a neural network operator", Front Mar Sci, 9, pp.1037697~ (2022)[DOI]
- Lu W, "Subsurface temperature estimation from remote sensing data using a clusteringneural network method", Remote Sens Environ, 229, pp.213~222 (2019)
- Matsumoto K, "Ocean tide models developed by assimilating TOPEX", POSEIDON altimeter data into hydrodynamical model : a global model and a regional model around Japan, 2000, pp.56, 567~581
- Nardelli BB, "A deep learning network to retrieve ocean hydrographic profiles from combined satellite and in situ measurements", Remote Sens, 12, pp.3151~ (2020)[DOI]
- O'Carroll AG, "Observational needs of sea surface temperature", Front Mar Sci, 6, pp.420~ (2019)[DOI]
- Shi X, "Convolutional LSTM network: a machine learning approach for precipitation nowcasting", NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems, pp.802~810 (2015)
- Su H, "Predicting subsurface thermohaline structure from remote sensing data based on long short-term memory neural networks", Remote Sens Environ, 260, pp.112465~ (2021)[DOI]
- Xiao C, "A spatiotemporal deep learning model for sea surface temperature field prediction using timeseries satellite data", Environ Model Softw, 120, pp.104502~ (2019)[DOI]