A Deep Learning Framework for Parameter Estimation in 1D Marine Ecosystem Model
Marine ecosystems play an increasingly critical role in both global climate regulation and the impacts of anthropogenic activities. Accurate marine biogeochemical numerical models are essential tools for understanding and predicting the complex dynamics of these systems. However, as model complexity...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/7/1228 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Marine ecosystems play an increasingly critical role in both global climate regulation and the impacts of anthropogenic activities. Accurate marine biogeochemical numerical models are essential tools for understanding and predicting the complex dynamics of these systems. However, as model complexity increases, so does the number of biological parameters and the uncertainties associated with them, which can substantially affect model performance. Consequently, efficient and reliable parameter estimation has become a key challenge in model development and application. In this study, we proposed a deep learning-based approach for parameter estimation in numerical models, utilizing deep neural networks to capture the complex nonlinear relationships between numerical model input parameters and output variables. A one-dimensional marine ecosystem model is employed as a case study to evaluate the effectiveness of the proposed method. The results demonstrate that (1) the deep learning approach provides an automated and adaptive solution for parameter estimation, significantly improving the efficiency of model calibration in engineering and scientific applications by enabling rapid identification of near-optimal parameter sets; and (2) the deep learning model effectively learns the underlying nonlinear mappings between inputs and outputs. During training, the mean squared error (MSE) loss exhibits a steady decline, with minor fluctuations in the early stages, ultimately converging to a stable value. The predicted parameter combinations show strong agreement with the reference numerical model outputs, yielding correlation coefficients exceeding 0.95. |
|---|---|
| ISSN: | 2077-1312 |