Data Mining Tools to Improve Buried Water Infrastructure Decision-Making

改善埋地水基础设施决策的数据挖掘工具

• 批准号: 9318-2012
• 负责人: Mcbean, Edward
• 金额: $ 1.75万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=509123
• 关键词: Data; Mining; Tools; Improve; Buried

Buried water infrastructure throughout Canada is deteriorating as the infrastructure approaches the end of its useful life. While remediation of components of the infrastructure is feasible, identifying the best remedial actions and prioritizing investment strategies between different portions of the water infrastructure, is extremely challenging. The challenges in decision-making arise since the rates of failure of water and sewer pipes are influenced by numerous external factors. Many existing approaches have relied upon nonlinear regression, an approach which is highly constrained by assumptions and constraints implicit in the methodology. This research will employ new data mining models with different types of architecture, to assess their capabilities to provide processing capabilities which are capable of performing massive parallel processing computations and are not constrained by the limiting assumptions existing in nonlinear regression.

随着基础设施的使用寿命接近尾声，整个加拿大的地下供水基础设施正在恶化。虽然修复基础设施的组成部分是可行的，但确定最佳补救行动并确定水利基础设施不同部分之间的投资战略的优先次序是极具挑战性的。由于给水和下水道的故障率受到许多外部因素的影响，在决策方面出现了挑战。许多现有的方法依赖于非线性回归，这种方法受到方法中隐含的假设和约束的高度限制。本研究将采用不同类型架构的新数据挖掘模型，以评估它们的处理能力，这些处理能力能够执行大规模的并行处理计算，并且不受非线性回归中存在的限制假设的限制。