基于物联网的自动化系统用于预测与水有关的疾病
Bhushankumar Nemade1, Kiran Kishor Maharana2, Vikram Kulkarni3
1Shree L.R. Tiwari Engineering College, Mumbai University, Mumbai, India.
Scientific reports
|November 28, 2024
概括
本研究介绍了一种物联网系统和机器学习模型,用于预测水源性疾病和预测水质趋势,旨在改善全球获得安全饮用水的机会.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 获得饮用水是一种基本权利,但每年有340万人死于水源性疾病,11亿人缺乏安全饮用水.
- 人类活动污染了水源,使得估计80%的疾病是水造成的.
- 工业化和发展并没有解决全球水危机,需要先进的解决方案.
研究的目的:
- 开发一个实时系统来监测水质和预测水传播疾病.
- 用先进的机器学习技术预测长期的水质趋势.
- 为解决影响全球健康的水资源短缺和污染的关键问题.
主要方法:
- 使用了西孟加拉州污染控制局 (WBPCB) 的实时数据集,其中包含17个功能.
- 提出了一个基于物联网 (IoT) 的系统,用于使用混合适应性神经模糊推理系统 (H-ANFIS) 实时收集数据.
- 采用机器学习算法,包括随机森林,XGBoost,AdaBoost用于分类和长短期记忆 (LSTM) 用于预测. 引入了TS-SMOTE用于数据增强.
主要成果:
- 分类模型实现了高精度:随机森林 (99.66%),AdaBoost (99.64%) 和XGBoost (99.52%).
- 对于pH参数的LSTM预测产生了0.1631.1的平均平方误差 (MSE).
- 物联网系统有效地实时收集数据,并识别了潜在的攻击.
结论:
- 拟议的物联网系统和机器学习模型在预测水源性疾病和预测水质方面表现出高效.
- 这项研究为确保可靠的饮用水供应和减轻健康风险提供了一个强大的框架.
- 新的TS-SMOTE方法提高了水质监测时间序列分析的性能.
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