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

当铁路排水基础设施出现故障时，由此产生的洪水可能会导致乘客延误和生命风险，并给网络铁路（NR）带来惩罚性成本，但也可能导致其他严重故障，如土地滑坡，正如最近在苏格兰东北部发生的严重脱轨事件中所看到的那样。NR和谢菲尔德大学（UoS）最近的合作已经开始揭示排水资产的性能和退化，以及由此对母公司资产的影响。然而，这一认识取决于对从周围集水区进入铁路系统的水量的准确和可靠的了解。目前的水文模型在大尺度上运行，以预测广泛的洪水区域，而不是在与单个铁路资产相关的尺度上对水流路径进行准确建模。数字地形模型的分辨率通常有限，可能会忽略较小规模的排水功能，如路边沟渠，可以大大改变一个小集水区。模型还受到渗透率、储存能力和水流路线等因素的严重不确定性的影响。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中的不确定性，从而能够在单个铁路资产层面上更准确地预测到达流量。该项目将由Nichols博士和Tait教授与Network Rail的合作伙伴共同监督。网络铁路将提供工业重点，使成功的申请人能够在工业和学术界之间的接口工作，在大学和公司度过大量时间