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Research progress in water quality prediction based on deep learning technology: a review.
Wenhao Li1,2, Yin Zhao1, Yining Zhu2,3
1School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China.
Environmental Science and Pollution Research International
|March 28, 2024
Summary
Accurate water quality forecasting is vital for resource management. Deep learning models show promise for predicting pollutant levels, aiding environmental protection efforts.
Area of Science:
- Environmental Science
- Computer Science
- Data Science
Background:
- Water is a critical, non-renewable resource essential for survival and development.
- Accurate water quality forecasting is key for informed environmental management and intervention.
- Deep learning offers powerful data analytics for water quality prediction.
Purpose of the Study:
- To review the application of deep learning in water quality forecasting.
- To explore single-model and mixed-model deep learning approaches.
- To identify optimization and data fusion strategies for improved prediction accuracy.
Main Methods:
- Comprehensive literature review of deep learning methodologies in water quality prediction.
- Analysis of single-model and mixed-model deep learning frameworks.
- Examination of optimization techniques and data fusion strategies.
Main Results:
- Deep learning models are increasingly deployed for water quality prediction.
- Various factors, including optimization and data fusion, impact model efficacy.
- Challenges include data scarcity and long-term prediction accuracy.
Conclusions:
- Deep learning shows significant potential for accurate water quality forecasting.
- Addressing limitations like data scarcity is crucial for future advancements.
- Refined algorithms and large-scale applications will enhance water resource management.
