Development of a river dissolved oxygen prediction model integrating spatial effects and multiple deep learning algorithm
Dissolved oxygen (DO) in rivers serves as a vital indicator for assessing aquatic environmental health, as it significantly influences the survival of aquatic organisms and the stability of ecosystems. To address the nonlinear, complex, and periodic nature of DO time series, a novel prediction frame...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002432 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Dissolved oxygen (DO) in rivers serves as a vital indicator for assessing aquatic environmental health, as it significantly influences the survival of aquatic organisms and the stability of ecosystems. To address the nonlinear, complex, and periodic nature of DO time series, a novel prediction framework is proposed, in which Wavelet Convolution (WTConv), a technique traditionally used in image processing, is applied for the first time to DO forecasting. A hybrid architecture, named RIME-VMD-FECAM-WTConv-Transformer, integrates RIME (Rooted in Rime-Ice), Variational Mode Decomposition (VMD), Frequency Enhanced Channel Attention Mechanism (FECAM), and Transformer. In this model, the DO series is first decomposed into multiple sub-series via VMD, and each sub-series is individually trained and predicted using the FECAM-WTConv-Transformer structure; the final output is obtained by aggregating the predictions. The model is benchmarked against several baselines and evaluated using RMSE, MAE, R2, and PICP, which are recorded as 0.2935, 0.2053, 0.9852, and 98.9 %, respectively—outperforming all competing models. Cross-validation, significance testing, out-of-sample prediction, and Shapley Additive Explanations (SHAP) are also conducted to ensure the model's stability, reliability, interpretability, and performance. In addition, wavelet transform is used to explore the temporal correlations between DO and meteorological and water quality variables, further enhancing the interpretability of the deep learning approach. Overall, the proposed model demonstrates excellent predictive performance and holds strong potential for supporting the scientific management of surface water resources. |
|---|---|
| ISSN: | 1574-9541 |