Environmental Risk Assessment Method Combining Set Pair Analysis and K-Means Clustering

The environmental risk assessment method combining Set Pair Analysis and K-means clustering is a comprehensive technical approach that integrates uncertainty analysis with data clustering techniques, effectively addressing complexity and ambiguity issues in environmental risk evaluation. This method utilizes Set Pair Analysis (SPA) to characterize relationships and differences among evaluation indicators, while employing the K-means clustering algorithm to classify assessment results into distinct risk levels, providing decision-makers with intuitive risk categorization.

In practical implementation, the entropy weight method is first applied to determine weights for various environmental indicators. Based on information entropy theory, the entropy weight method objectively reflects data dispersion characteristics while avoiding biases from subjective weighting. Subsequently, Set Pair Analysis constructs connection degrees between evaluation objects and ideal solutions, calculating comprehensive proximity degrees to quantify environmental risk levels. Finally, the K-means clustering algorithm automatically classifies comprehensive evaluation values into multiple risk categories (such as low, medium, and high risk), facilitating cross-regional comparisons and management strategy development.

When implemented in MATLAB, this method leverages efficient matrix operations and built-in clustering toolboxes while incorporating custom SPA calculation workflows to establish a complete analytical pipeline from data preprocessing to result visualization. Key MATLAB functions like kmeans() for clustering optimization and array operations for SPA calculations enhance computational efficiency. The method's advantages include objective indicator weighting, robust evaluation processes, and intuitive result interpretation, making it particularly suitable for multi-indicator, multi-sample environmental risk assessment scenarios.