机器学习模型的开发,以估计基于生物性干模型的预测,淡水中铜的无效度
Jiwoong Chung1,2, Geonwoo Yoo1, Jae-Seong Jo1
1Environmental Health & Safety Research Institute, EH Research & Consulting, Incheon, Republic of Korea.
Environmental toxicology and chemistry
|June 28, 2023
概括
深度神经网络模型通过简化生物连接物模型 (BLM) 的数据需求来改善铜 (Cu) 风险评估. 这些模型使用易于获得的水化学数据来增强无效度 (PNEC) 的预测.
科学领域:
- 环境化学环境化学
- 生态毒理学 生态毒理学
- 计算建模 计算建模
背景情况:
- 铜 (Cu) 生物联体模型 (BLM) 对于生态风险评估至关重要,但需要大量的水化学数据.
- 获得Cu BLM的综合数据对于常规水质监测计划来说可能是一个挑战.
研究的目的:
- 为铜开发优化预测无效度 (PNEC) 估计模型.
- 评估使用低输入变量深度神经网络 (DNN) 进行Cu PNEC预测的可行性.
主要方法:
- 开发了三种DNN模型,输入数据要求各不相同 (BLM变量完整,不包括性,使用电导率作为替代品).
- 将DNN模型的预测性能与现有的PNEC估计工具 (查看表,回归方法) 相比较.
主要成果:
- 三种DNN模型在各种淡水数据集 (韩国,美国,瑞典,比利时) 中展示了Cu PNEC的优异预测准确性.
- DNN模型有效地捕获了水化学和Cu生物可用性之间的非线性关系.
结论:
- DNN模型为基于Cu BLM的风险评估提供了一种灵活而准确的方法,可以适应变化的数据可用性.
- 这些优化模型有助于在环境监测中更广泛地应用Cu风险评估.
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