Environment-compatible heavy metal risk prediction method created with multilevel ensemble learning.

Accurate health risk prediction (HRP) is an effective means of reducing the hazards of heavy metal (HM) exposure. It can address the drawbacks of lag and passivity faced by health risk assessment. This study innovatively proposed an HRP method, MEL-HR, based on multilevel ensemble learning (MEL) technology and environment compatibility. We conducted point and interval prediction experiments on health risks using 490 sets of data covering 17 environment factors. The point prediction results indicated that when the model predicts HI and TCR, the R values were 0.707 and 0.619, respectively. For P5, P50, and P95 in interval prediction, the R values of the model were 0.706, 0.703, and 0.672 for HI, and that for TCR were 0.620, 0.607, and 0.616, respectively. The analysis of feature importance indicated that, in addition to HM factors, longitude, mining area coefficient, and soil organic matter were key environmental factors affecting the MEL-HR model. Comparative experiments showed that compared to soil HMs-based MEL-HR, environment compatibility-based MEL-HR has improved the accuracy for HI and TCR by 19.83 % and 40.36 % for the point prediction and 22.06 % and 40.01 % for interval prediction. This study can provide technical support for targeted and resilient prevention and control of health risks.