Integrated ecological risk assessment with composite risk index and machine learning: spatiotemporal analysis of Beshilo Watershed, Northwest Ethiopia

Abstract

Climate change and anthropogenic activities pose significant threats to terrestrial ecosystem functions and processes, which greatly increase ecological risk. Therefore, investigating the spatiotemporal dynamics of ecological risk and its driving factors in the Beshilo River Watershed is essential for evaluating ecological conditions and supporting sustainable ecosystem management. This study integrates the composite ecological risk index (ERI) with machine learning (ML) and ensemble learning (EL) approaches to assess ecological risk. Ten driving factors were analyzed using the Geodetector and Shapley Additive Explanations (SHAP) to explore their influence on ecological risk. The results revealed that high ecological risk was primarily concentrated in the western and central parts of the watershed. Furthermore, the ML and EL models outperform the ERI in predictive accuracy. Among the ensemble models, the Random Forest -Extreme Gradient Boosting (XGBoost) model achieved the best performance (R2 = 0.976, RMSE = 0.0028, MAE = 0.0024). Trend analysis using the Mann-Kendall test and Sen’s slope indicates a statistically significant declining trend in ecological risk, despite noticeable temporal fluctuations during the study period. The normalized vegetation index, leaf area index, elevation, soil and precipitation were identified as the dominant drivers of ecological risk. This study provides valuable insights for developing effective ecological restoration and conservation strategies in the watershed.