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一个集体学习模型用于预测水管泄漏情况.
Ahmed Ali Mohamed Warad1, Khaled Wassif2, Nagy Ramadan Darwish3
1Department of Information Systems and Technology, Faculty of Graduate Studies for Statistical Research, Cairo University, Cairo, Egypt. ahmedwarad.2010@gmail.com.
Scientific reports
|May 9, 2024
概括
这项研究引入了一种优化的组合模型,用于预测水管泄漏情况,其性能优于传统的包装和增强方法. 新模型显著提高了管道故障率的预测准确性.
科学领域:
- 环境工程 环境工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 包装和提升等组合方法被广泛用于减少预测模型中的差异和偏差.
- 水管泄漏预测对于基础设施管理至关重要,但大规模的数据集带来了独特的挑战.
- 优化组合学习用于管道故障率预测尚未得到广泛探索.
研究的目的:
- 开发和评估一个基于优化集团学习的模型,用于使用大型管道故障数据集进行水管泄漏预测.
- 通过在整体重量优化过程中调整超参数来提高预测水管泄漏的准确性.
- 为了比较拟议模型的性能与标准包装和增强组合技术.
主要方法:
- 开发了一个优化集体学习模型,将基础学习者的超参数调纳入.
- 该模型在大量水管故障数据集上进行了训练和评估.
- 使用包括根-平均-平方误差 (RMSE),平均平方误差 (MSE),平均绝对误差 (MAE) 和确定系数 (R2) 在内的指标来评估性能.
主要成果:
- 拟议的优化组合模型与包装和增强组合模型相比,显示出更高的预测准确性.
- 该模型在第14次代中实现了对水管故障率的最佳预测.
- 最优化的模型产生了最低的RMSE (0.00231) 和MAE (0.00071513).
结论:
- 优化集体学习模型在水管泄漏预测准确度上提供了显著的改进.
- 在集体重量优化中的超参数调整对于提高预测性能是有效的.
- 开发的模型为预测水管故障率提供了强大的解决方案,这对于公用事业管理至关重要.
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