A deep learning interpretable model for river dissolved oxygen multi-step and interval prediction based on multi-source data fusion
Shanghai Ocean University · Wenzhou University
Abstract
• A multi-source data fusion model of meteorology and hydrology for DO prediction is proposed. • The model has an excellent valley, multi-step ahead and interval prediction capability. • Machine learning interpretability analysis of the impact of external factors on water quality. • Improved sparrow optimization algorithm with better search efficiency and effectiveness. Water bodies experiencing excessively low dissolved oxygen (DO) concentrations cannot sustain aquatic life and disrupt ecosystem balance, whereas overly high concentrations induce eutrophication, deteriorating the water environment's health. DO monitoring and safeguarding have perennially been paramount for global environmental protection…
Citation impact
- FWCI
- 23.32
- Percentile
- 100%
- References
- 72
Authors
4Topics & keywords
- Interpretability
- Benchmark (surveying)
- Computer science
- Decomposition
- Artificial intelligence
- Sequence (biology)
- Predictive modelling
- Machine learning