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在水管道中使用特征工程和级联集成的多步时间到故障预测
Beenish Bakhtawar1, Tarek Zayed1, Husnain Arshad1
1Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
Water research
|July 27, 2025
概括
通过分析历史数据和动态特征的新模型,预测水管道故障得到了改进. 这种方法提高了水分网的维护和恢复规划的准确性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 水资源管理 水资源管理
- 数据科学数据科学数据科学
背景情况:
- 准确预测水管道故障时间对于水分网的有效维护和恢复策略至关重要.
- 现有的模型在整合故障历史和预测连续故障方面扎,这限制了它们的实际应用.
- 当前的时间到故障模型的准确性经常受到历史数据中对重大故障预测因素的调查不足的损害.
研究的目的:
- 开发先进的模型,以更高的准确度预测水管道故障时间.
- 解决现有模型在整合故障历史和预测连续故障方面的局限性.
- 从历史和动态数据中识别和利用管道故障的最重要的预测因素.
主要方法:
- 开发定制的天气指数和其他交互功能,以捕捉基于时间的恶化影响.
- 使用遗传算法 (GA) 进行自动特征选择,以优化模型性能.
- 实施级联组合模型,用于预测单个管道中的多次连续故障 (第一,第二,第三).
- 通过保留和样本外测试进行验证.
主要成果:
- 功能工程和基于GA的功能选择导致模型性能增加了20-50%.
- 开发的模型实现了1.4-0.5的平均绝对误差 (MAE) 范围,以优化特征选择.
- 级联组合模型表现出优异的性能 (MAE:0.8-1.1) 与替代多输出模型相比,用于顺序失效预测.
- 开发了一个基于Web的应用程序,以证明新型建模方法的实际实用性.
结论:
- 该研究提出了一种新的建模模式,用于在水管道中高性能故障时间预测.
- 该方法提供管道段的微层分析,提供对复杂特征相互作用和恶化率的洞察.
- 开发的模型和方法显著改善了对单个管道故障的预测,支持更好的基础设施管理.
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