Intelligent prediction method for groundwater fluorine pollution based on evolutionary algorithm driving
By using an evolutionary algorithm-driven feature subspace ensemble learning model, which automatically optimizes feature subsets and combines them with multiple base learners, the accuracy and stability issues of groundwater fluoride pollution prediction in complex environments are solved, achieving efficient feature utilization and improved prediction accuracy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SICHUAN ACAD OF GRASSLAND SCI
- Filing Date
- 2026-04-03
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies struggle to effectively utilize multidimensional environmental factor information in complex environments to achieve high-precision and robust prediction of groundwater fluoride pollution. Furthermore, feature selection relies on human experience, and ensemble learning methods lack targeted optimization capabilities.
An evolutionary algorithm-driven feature subspace ensemble learning model is adopted. Through population initialization, fitness evaluation, selection, crossover, mutation and elite retention operations, the feature subset is automatically optimized and combined with multiple base learners for ensemble prediction.
It achieves efficient automatic feature selection, improves the model's generalization ability and stability, enhances the accuracy and robustness of groundwater fluoride pollution prediction, and reduces reliance on human experience.
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