Prediction of Soil Pollution Risk Based on Machine Learning and SHAP Interpretable Models in the Nansi Lake, China

Toxics, Journal Year: 2025, Volume and Issue: 13(4), P. 278 - 278

Published: April 5, 2025

To assess and predict the Nansi Lake soil pollution risk, we evaluate environmental quality in region using machine learning techniques, combined with SHapley Additive exPlanations (SHAP) model for interpretability. The primary objective was to level of caused by heavy metals, incorporating traditional Pollution Load Index (PLI) Potential Ecological Risk (PERI) methods. Through integration statistical characteristics, PLI, PERI evaluations, a new assessment method created, categorizing into “Class0—no risk”, “Class1—low “Class2—high risk”. Various models, including Support Vector Machine (SVM), Decision Tree Classifier (DT), Random Forest (RF), XGBoost, were employed based on these indices. XGBoost demonstrated highest accuracy, achieving prediction accuracy 93%. SHAP analysis further applied explain determined that accumulation key pollutants such as cadmium (Cd) mercury (Hg) may significantly produce targeted management needs be developed features.

Language: Английский

Explainable artificial intelligence model for accident severity modeling

Asian Journal of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 9, 2025

Exploring land cover change impacts on ecosystem services using machine learning technique and scenario simulation: case study of the Upper Citarum River Basin, Indonesia

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(3)

Published: April 11, 2025

Language: Английский