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Updated: Jun 7, 2025
12:44
Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
8.0K
Water environment risk prediction method based on convolutional neural network-random forest
Yanan Zhao1, Lili Zhang1, Yue Chen1
1School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China.
Marine Pollution Bulletin
|November 13, 2024
Summary
This study presents a new method combining Convolutional Neural Networks (CNN) and Random Forest (RF) to accurately predict water environmental risks. The integrated approach significantly improves prediction accuracy and aids in safeguarding water resources.
Area of Science:
- Environmental Science
- Data Science
- Ecology
Background:
- Urbanization and industrialization increase aquatic environment risks, threatening water resources and ecosystem health.
- Accurate water environmental risk prediction is vital for pollution source identification, resource protection, and policy making.
Purpose of the Study:
- To develop an innovative prediction methodology for water environmental risks.
- To enhance the accuracy and applicability of water environmental risk assessments.
Main Methods:
- Integration of Convolutional Neural Networks (CNN) for spatial feature extraction.
- Application of Random Forest (RF) for multivariate data analysis.
- Merging prediction results with satellite imagery for visualization.
Main Results:
- Enhanced coefficient of determination (R²) by 5.8%.
- Reduced Mean Absolute Error (MAE) by 21.5%.
- Decreased Mean Bias Error (MBE) by 41.5% and Root Mean Square Error (RMSE) by 56.82%.
Conclusions:
- The proposed CNN-RF methodology offers a significant advancement in predicting water environmental risks.
- The approach facilitates intuitive visualization and enhances decision-making for complex environmental data.
- The study elucidates evolving trends in water environmental risks, supporting sustainable water resource management.
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