Data driven hydrological modelling for railway water management

铁路水管理的数据驱动水文建模

• 批准号: 2784410
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2784410
• 关键词: Data; driven; hydrological; modelling; railway

When railway drainage infrastructure fails, resulting flooding can cause delay and risk to life for passengers, and penalty costs for Network Rail (NR), but can also cause other severe failures such as land slips, as seen in the recent serious derailment event in North-East Scotland.Recent collaboration between NR and The University of Sheffield (UoS) has begun to shed light on the performance and degradation of drainage assets, and the resulting impacts on parent assets. However, this understanding is contingent on accurate and reliable knowledge of the volume of water entering the railway system from the surrounding catchment.Current hydrological models operate at large scale to predict broad areas of flooding, and are not developed for accurate modelling of flow paths at scales relevant to individual railway assets. Digital terrain models are typically limited in resolution and can omit smaller scale drainage features such as roadside ditches that can substantially alter a small catchment. Models also suffer from deep uncertainty in factors such as infiltration rate, storage capacities and flow routing. This project will use a data-driven approach to address the uncertainty in water transport processes from rainfall to railway, enabling more accurate prediction of arrival flow volumes at the individual railway asset level.The project will be supervised by Dr Nichols and Professor Tait in collaboration with partners from Network Rail. Network Rail will provide industrial focus and enable the successful applicant to work at the interface between industry and academia, with substantial time spent both at the university and the company

When railway drainage infrastructure fails, resulting flooding can cause delay and risk to life for passengers, and penalty costs for Network Rail (NR), but can also cause other severe failures such as land slips, as seen in the recent serious derailment event in North-East Scotland.Recent collaboration between NR and The University of Sheffield (UoS) has begun to shed light on the performance and degradation of drainage assets, and the resulting对父母资产的影响。但是，这种理解取决于对从周围流域进入铁路系统进入铁路系统的水数量的准确知识。汇总的水文模型大规模运行以预测洪水的广泛领域，并且没有开发出来以准确地建模与单个铁路资产相关的尺度上的流动路径。数字地形模型通常在分辨率上受到限制，并且可以省略较小的规模排水特征，例如路边沟渠，这些沟渠可能会大大改变小流域。模型还遭受了渗透率，存储能力和流程路线等因素的深刻不确定性。该项目将采用数据驱动的方法来解决从降雨到铁路的水运输过程的不确定性，从而可以更准确地预测到各个铁路资产水平的到达流量。网络铁路将提供工业重点，并使成功的申请人能够在行业和学术界之间的界面工作，并在大学和公司花费了大量时间