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对水质预测模型的对抗性易感性分析
Jaya Zalte1,2, Ashita Rai3, Harshal Shah4
1Computer Science Engineering Department, Faculty of Engineering & Technology, Parul University, Vadodara, Gujarat, India.
Scientific reports
|October 30, 2025
概括
机器学习使用随机森林和包装分类器准确预测水污染. 模型显示了对抗性攻击的脆弱性,强调了在公共卫生水质监测中需要强大的人工智能.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 人工智能的人工智能
背景情况:
- 水质对健康和可持续性至关重要.
- 城市化和工业化增加了水源性疾病的风险.
- 在水源中检测病原体对于预防疾病至关重要.
研究的目的:
- 使用机器学习调查古吉拉特邦的水污染模式.
- 确定影响水质的关键病原体.
- 评估AI模型对数据腐败的稳定性.
主要方法:
- 应用机器学习分类器:HistGradientBoosting,随机森林,AdaBoost,包装,决策树和LSTM.
- 使用了SHapley添加式解释 (SHAP) 进行特征解释.
- 使用对抗性扰动 (FGSM,PGD) 和对抗性训练评估模型弹性.
主要成果:
- 随机森林和包装分类器在预测水质方面取得了98.53%的准确性.
- 敌对攻击导致高达56%的绩效下降,在敌对训练后降至10%.
- 与传统的机器学习模型相比,神经网络模型对对抗性攻击具有更强的弹性.
结论:
- 机器学习,特别是随机森林和包装,在评估水质方面表现出很高的准确性.
- 人工智能模型需要对抗性训练,以确保在数据噪声的真实环境中可靠性.
- 开发弹性AI系统对于有效的公共卫生和水安全管理至关重要.
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