Assessing and Modelling the Influence of Household Characteristics on Per Capita Water Consumption
Assessing and Modelling the Influence of Household Characteristics on Per Capita Water Consumption
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DOI：
10.1007/s11269-016-1314-x
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发表时间：
2016-07-01
影响因子：
4.3
通讯作者：
Savic, Dragan A.
中科院分区：
文献类型：
--
作者：
Hussien, Wa'el A.;Memon, Fayyaz A.;Savic, Dragan A.
理想情况下，可持续的城市供水管理需要对人均消费量进行准确的循证估计，并充分了解影响消费量的因素。这些信息可用于改进水需求预测。发达国家的用水模式已得到广泛调查。然而，对发展中国家知之甚少。本文调查了发展中国家城市地区典型的不同类型家庭的人均用水量。为407个家庭执行了数据收集方案，以提取关于家庭特征、用水者行为和强度以及室内和室外用水活动性质的信息。对数据的严格统计分析表明，人均用水量随着收入的增加而增加：低收入、中等收入和高收入家庭分别为241、272和290升/人/天。此外，研究结果表明，人均消费量随着家庭中成年女性成员的数量而增加，近三分之一的消费量是通过水龙头。收集的数据已被用来开发统计模型，使用两种不同的回归技术：多元线性（STEPWISE）和进化多项式回归（EPR）。在开发的模型中纳入人口参数大大提高了预测精度。两个最好的表现模型被用来预测城市的用水需求，使用四个未来的情景：市场力量，堡垒世界，政策改革和大转型。结果表明，生活用水需求将是最高的堡垒世界的情况下，由于人口的增加和规模的建成区。
Sustainable urban water supply management requires, ideally, accurate evidence based estimations on per capita consumption and a good understanding of the factors influencing the consumption. The information can then be used to achieve improved water demand forecasts. Water consumption patterns in the developed countries have been extensively investigated. However, very little is known for the developing world. This paper investigates per capita water consumption resulting from water use activities in different types of households typically found in urban areas of the developing world. A data collection programme was executed for 407 households to extract information on household characteristics, water user behaviour and intensity and the nature of indoor and outdoor water use activities. The rigorous statistical analysis of the data shows that per capita water consumption increases with income: 241, 272 and 290 l/capita/day for low, medium and high income households, respectively. Additionally, the results suggest that per capita consumption increases with the number of adult female members in the household and almost one-third of consumption is via taps. The collected data has been used to develop statistical models using two different regression techniques: multiple linear (STEPWISE) and evolutionary polynomial regression (EPR). The inclusion of demographic parameters in the developed models considerably improved the prediction accuracy. Two of the best performing models are used to forecast the water demand for the city, using four future scenarios: market forces, fortress world, policy reform and great transition. The results suggest that the domestic water demand would be highest in the fortress world scenario due to the increase in population and size of built-up area.