## 3 Methodology  
### Multicollinearity Assessment  
The reliability and accuracy of the developed SSMs depend critically on the careful selection of conditioning factors that closely align with site-specific environmental variables. This necessitates a rigorous assessment of multicollinearity to detect and address interdependencies among the selected variables, ensuring robust model performance. This step is crucial before initiating classifier training to circumvent the curse of dimensionality. Multicollinearity, if not addressed, can lead to several issues such as overfitting, model instability, incorrect interpretation of feature importance, computational difficulties, and redundancy in the dataset. Overfitting occurs when the model performs well on training data but poorly on unseen data due to high correlation among predictors [62]. Model instability arises when small changes in the data lead to large fluctuations in the model's coefficients, making the results unreliable [63]. Incorrect interpretation of feature importance can mislead decision-making, especially in explainable AI methods like SHAP [64]. Computational difficulties may arise in matrix inversion processes, particularly in linear models, leading to numerical instability [65]. Finally, redundancy in the dataset increases model complexity without adding meaningful information, reducing the model's efficiency [66].  
Scholars commonly employ a multicollinearity assessment to identify and address problematic conditioning factors, as high correlations among predictors can lead to unreliable model outcomes and misinterpretation of feature importance [67; 68]. Specifically, multicollinearity can inflate the variance of regression coefficients, making it difficult to assess the individual contribution of each predictor. To mitigate these issues, we conducted a multicollinearity assessment using two primary measures: the Variance Inflation Factor (VIF) and Tolerance (TOL). VIF quantifies how much the variance of an estimated regression coefficient increases due to multicollinearity, while TOL measures the proportion of variance in a predictor that is not explained by other predictors. If any chosen conditioning factor exhibits a VIF surpassing 10 and/or a TOL below 0.1, it indicates the presence of multicollinearity within the dataset, necessitating the removal of these factors until VIF and  
Figure 6: Environmental and anthropological factors: (**a**) WDS; (**b**) LDU; (**c**) PST; (**d**) PRD; (**e**) NDVI; (**f**) SDP.  
TOL values achieve acceptable levels. This approach ensures that the final set of predictors is free from significant interdependencies, thereby enhancing the model's robustness and interpretability.  
In addition, we conducted Pearson's correlation coefficient calculations to supplement the multicollinearity assessment. These coefficients cover a scale from \(-1\) to \(+1\), where values approaching \(+1\) signify a significant positive correlation between two factors, while values near \(-1\) denote a strong negative correlation. When the values are near zero, it suggests independence between two variables. In numerous prior research, the benchmarks of \(+0.8\) and \(-0.8\) have been commonly acknowledged as thresholds, signifying that values surpassing these benchmarks may give rise to challenges associated with multicollinearity [69].