使用机器学习模型来预测市政废水的剂量效应曲线,以预测斑马鱼胚胎毒性
Mengyuan Zhu1, Yushi Fang1, Min Jia1
1State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China.
Journal of hazardous materials
|February 3, 2025
概括
机器学习模型预测了市政废水中的斑马鱼胚胎毒性,减少了75%的实验工作量. 该工具通过有效评估有毒风险,有助于废水管理和环境政策制定.
科学领域:
- 环境科学 环境科学
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
背景情况:
- 市政废水对水生环境构成重大生态风险.
- 使用生物分析的传统毒性评估是耗时的,劳动密集的和昂贵的.
- 开发有效的预测废水毒性的方法对于有效的环境管理至关重要.
研究的目的:
- 开发机器学习模型,用于预测斑马鱼胚胎的废水剂量效应曲线.
- 确定影响斑马鱼胚胎毒性的关键水质参数.
- 为了减少与毒性评估相关的实验工作量.
主要方法:
- 从中国的176个废水处理厂收集了进水和废水样本.
- 分析了7个化学参数和8个相对丰富因子 (REF) 的斑马鱼胚胎毒性.
- 使用斯皮尔曼等级相关性和最大相关性/最小冗余性用于特征选择,开发决策树,随机森林和梯度增强决策树 (GBDT) 模型.
主要成果:
- 在REF2和REF25的氨含量和毒性被确定为关键输入特征.
- GBDT模型实现了0.91的R2和27.91%的MAPE.
- 整合剂量效应曲线模式优化了GBDT模型,达到14.74%的最低MAPE,并将工作量减少了75%以上.
结论:
- 开发的机器学习模型准确地预测了市政废水的斑马鱼胚胎毒性剂量效应曲线.
- 这种方法显著减少了实验工作量,为废水管理提供了有价值的工具.
- 将环境专业知识与机器学习相结合,为评估有毒风险和为环境政策提供信息提供了新的途径.
相关概念视频
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
01:18Steps in Outbreak Investigation
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis: