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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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香港下水道和雨水管道的多层恶化评估模型
Sherif Abdelkhalek1, Tarek Zayed1
1Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
Journal of environmental management
|September 9, 2023
概括
这项研究开发了人工智能模型来预测香港的下水道和雨水管道的状况,帮助主动维护. 随机森林模型在评估管道健康状况和优化基础设施管理方面显示出最高的准确性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 环境工程 环境工程
- 数据科学数据科学数据科学
背景情况:
- 污水和雨水网络面临着因老化,环境因素和交通等因素的恶化.
- 维护这些网络对于防止环境污染和基础设施故障至关重要.
- 恶化评估模型为主动基础设施管理提供了一种具有成本效益的方法.
研究的目的:
- 为香港的下水道和雨水管道开发准确的恶化评估模型.
- 确定影响管道恶化的关键因素.
- 为优化检查和维护提供决策工具.
主要方法:
- 确定了关键的恶化因素,并从排水服务部 (DSD) 和香港政府来源收集数据.
- 开发了一种多层建模方法:1层用于故障/无故障分类,2层用于管道分级.
- 测试了人工智能技术,包括随机森林,神经网络和支持矢量机器 (SVM).
主要成果:
- 随机森林模型显示了对管道状况的最高预测准确度.
- 神经网络在预测管道恶化方面也表现强.
- 敏感性分析强调了诸如年龄等因素对管道性能的影响.
结论:
- 开发的AI模型为香港的DSD和顾问提供了强大的工具.
- 基于模型预测,可以实施优化的检查和维护策略.
- 地下基础设施的积极管理对于城市的弹性至关重要.
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