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Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds
1State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China.
Journal of Environmental Management
|May 10, 2024
Summary
This study reveals spatial patterns in aquatic biological integrity in semi-arid regions using machine learning. Annual average temperature significantly impacts phytoplankton integrity, with distinct north-south variations identified.
Area of Science:
- Ecology
- Environmental Science
- Data Science
Background:
- Semi-arid regions face unique challenges in maintaining aquatic biological integrity.
- Compound environmental stress complicates the study of spatial patterns in these ecosystems.
Purpose of the Study:
- To develop a spatial analysis and diagnosis method for aquatic biological integrity in semi-arid regions.
- To reveal spatial differentiation patterns and causes of changes in aquatic biological integrity.
- To apply machine learning and statistical analysis for ecological assessment.
Main Methods:
- Combined XGBoost-SHAP (Explainable AI) with Fuzzy C-means clustering (FCM).
- Analyzed spatial variations in phytoplankton species number, diversity, and integrity index (P-IBI).
- Identified key environmental drivers influencing aquatic biological integrity.
Main Results:
- Significant spatial variations in phytoplankton integrity were observed across the Wei River Basin (WRB).
- Annual average temperature (AT) was the primary driver of P-IBI spatial divergence.
- Complex interactions between temperature, hydrology, and water quality shaped P-IBI distribution.
- FCM identified distinct north-south gradient disparities, defining four ecological zones.
Conclusions:
- The XGBoost-SHAP and FCM approach effectively diagnoses spatial heterogeneity in aquatic biological integrity.
- Findings provide scientific boundaries for conservation and management strategies in semi-arid aquatic ecosystems.
- The methodology is adaptable for analyzing spatial variations in diverse environmental contexts.
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