Projections of municipal water demand with ANN and SD models under climate change

气候变化下城市用水需求的ANN和SD模型预测

• 批准号: 531171-2018
• 负责人: Davies, Evan
• 金额: $ 1.8万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=648729
• 关键词: Projections; municipal; water; demand; ANN

Water utilities across North America have observed declining residential water consumption over the past three**decades. Because they typically bill for water use on a per-unit-volume basis, such reductions in use have**financial implications. Further, water rates are usually set years ahead of time, so that inaccurate monthly to**annual predictions compound the financial effects of reduced water use. On shorter time scales of hours to**weeks, water utilities also make operational decisions to meet municipal demands. Short term forecasts help to**plan maintenance and operating schedules for system infrastructure and inform decisions about water levels**and drawdowns for reservoirs, and they tend to be dominated by weather. Thus, accurate prediction of**municipal water use is important for both short-term (operational) and long-term (planning) aspects of urban**water management.**This project will 1) provide forecasting models for both daily (operational) and weekly (planning) water**demands for EPCOR in Edmonton, Alberta, and 2) incorporate the weekly demands into a unique decision**support tool for long-term municipal water management. The two forecasting models will be developed using**Artificial Neural Networks (ANN), which have been applied widely for water demand projections. ANNs offer**excellent predictive ability and are relatively straightforward to develop and validate. Next, the planning**version of the ANN model will be used to revise an existing municipal water management model, called the**Calgary Water Management Model (CWMM), which will be set up for Edmonton. The CWMM can be used to**support municipal planning horizons of years to decades and is particularly useful for assessing the effects of**population growth, climate change and the effects of conservation policies and technological change over the**long term.

过去三**十年中，北美各地的自来水公司观察到住宅用水量不断下降。由于他们通常按单位用水量计费，因此用水量的减少会产生**财务影响。此外，水费通常提前数年设定，因此每月到每年的预测不准确会加剧用水量减少的财务影响。在数小时至**周的较短时间范围内，自来水公司还做出运营决策以满足市政需求。短期预测有助于**规划系统基础设施的维护和运营计划，并为有关水位**和水库水位下降的决策提供信息，而且它们往往受天气影响。因此，准确预测**市政用水对于城市**水资源管理的短期（运营）和长期（规划）方面都很重要。**该项目将 1) 提供日常（运营）用水的预测模型) 和艾伯塔省埃德蒙顿 EPCOR 的每周（规划）用水需求**，以及 2) 将每周需求纳入到长期市政用水管理的独特决策**支持工具中。这两个预测模型将使用**人工神经网络（ANN）开发，该网络已广泛应用于水需求预测。人工神经网络提供**出色的预测能力，并且开发和验证相对简单。接下来，ANN模型的规划**版本将用于修改现有的市政水资源管理模型，称为**卡尔加里水资源管理模型（CWMM），该模型将为埃德蒙顿建立。 CWMM 可用于**支持数年至数十年的市政规划视野，对于评估**人口增长、气候变化以及保护政策和技术变革的长期影响特别有用。