局域法邻近点选取对供水量预测精度的影响

混沌局域法预测模型适用于非线性、非平稳的城市日供水量预测,而邻近相点个数的选取对该模型预测精度有直接影响。传统方法通常以嵌入维m作为参考值,凭经验选取m+1个邻近相点,且仅使用欧式距离法计算当前相点距离,无法反映相点的运动趋势,易引入伪邻近相点,导致预测精度的降低。鉴于此,将演化追踪法引入城市日供水量预测,通过挖掘邻近相点的历史演化规律对参考样本进行优选,以提高预测精度。最后,采用实际日供水量数据验证所提出方法,结果表明,运用演化追踪法优选邻近相点能显著提高日供水量预测精度,预测平均绝对误差由2.501%降低到1.683%。     

混沌局域法与神经网络组合供水量预测

城市供水量是非线性、非平稳时间序列,组合预测模型能获得更高精度预测结果。通过深入分析混沌局域法与神经网络预测模型特点,提出了一种新的组合预测模型。首先,应用混沌局域法对城市日供水量进行初预测,然后,应用神经网络对预测结果进行修正。由于所提出的组合模型利用了混沌局域法及神经网络进行优势互补,能同时提高预测精度与计算效率。为验证所提出组合预测模型的可行性,采用某市7a实测供水量数据,对混沌局域法、BPNN、RBF及GRNN神经网络4种单一预测模型及相应的3种组合模型预测精度进行定量分析,结果表明,组合预测模型精度都高于对应单一预测模型,混沌局域法与GRNN神经网络组合模型预测精度最高,且运算时间远低于单一神经网络模型运算时间。     

基于GIS的城市供水管网爆管预测预警信息系统

城市供水管网爆管预测预警信息系统以地理信息系统(GIS)为基本框架,加载管网基本属性及供水系统运行数据,采用生存分析和贝叶斯定理建立爆管模糊预测模型;将GIS空间分析功能引入爆管规律分析与研究,建立了爆管的缓冲区分析模型和关闸搜索分析模型,对爆管在空间的分布规律进行分类和综合评价;将预警理论应用于爆管预防日常管理,提出爆管预警指标,结合不同爆管环境、条件,制定合理警限,划分警级,进行爆管状态预警。该系统可为管网管理者提供爆管查询统计、空间分析和预测预警等快速、多层次和直观的信息,以保证管网供水安全。     

An ARIMA-NARX hybrid model for forecasting urban water consumption (case study: Tehran metropolis)

ABSTRACT

In this research, an ARIMA-NARX (Autoregressive Integrated Moving Average-Nonlinear Auto-Regressive eXogenous) hybrid model is proposed to forecast daily Urban Water Consumption (UWC) for Tehran Metropolis. The linear and nonlinear component of the UWC was forecast by ARIMA as a linear forecasting model and the artificial neural network as a nonlinear forecasting model, respectively. An alternative hybrid model including sunshine hour in addition to the previous studies’ predictors (the minimum, maximum and average temperature, relative humidity and precipitation) was selected as the superior alternative model. Then, the performance of proposed model was compared with ARIMA and NARX models. The results showed that the hybrid model, which benefits from capability of both linear and nonlinear models, has a higher accuracy than the other two models in forecasting UWC. Therefore, the proposed hybrid model has better results in UWC forecasting and, as a consequence, better urban water reservoir management will be provided.     

城市供水管网漏损时间的预测模型研究

城市供水企业迫切需要加强给水管网的漏损管理，以减少漏损水量和提高经济效益。在对华北某市供水管网漏损数据进行统计和分析的基础上，按照管段实际发生漏损次数分两种情况建立了供水管网漏损时间的预测模型，对漏损次数≤4次的管段采用基于SAS系统的多元线性回归方法，对漏损次数〉4次的管段则采用灰色预测方法。经实例验证，多元线性回归方法预测的平均相对误差为21％，灰色预测方法预测的平均相对误差〈6％，整套模型的精度可满足城市供水管网漏损宏观管理的需要，能够提高管网漏损防治的效率。     

Fast network multi-objective design algorithm combined with an a posteriori procedure for reliability evaluation under various operational scenarios

This paper presents a methodology for the optimal design of water supply networks. It features a multi-objective optimisation (aimed at minimising costs and maximising resilience) and a subsequent ‘retrospective’ evaluation of network reliability under various operational scenarios. The multi-objective optimisation is based on an algorithm specifically developed for the design of real networks which feature a very high number of nodes and pipes. The ‘retrospective’ evaluation of network reliability is assessed considering resilience contrasted with several other indexes adopted to describe the operational performance of the network under critical scenarios such as segment isolation or hydrant activation, and different water demand conditions. In the applications two case studies, made up of a simple benchmark network and a real network respectively, are considered for the multi-objective optimisation; the ‘retrospective’ evaluation of reliability is performed only on the real network. The latter example clearly highlights that the procedure proposed allows reliability and performance to be offset against cost, consenting informed choice of the optimal network configuration.     

演化追踪法优化相空间的SVM供水量预测模型

相空间重构的支持向量机预测模型应用十分广泛,在城市供水量预测方面也占据着重要地位,传统的预测模型趋向于将重构的相空间整体带入,这样可能存在引入无效相点从而影响预测精度的问题,基于此将演化追踪法引入相空间重构的预测模型对有效相点进行筛选,优化预测模型的训练样本,达到提高预测精度目的。利用MATLAB编程软件将演化追踪法用于城市供水量的预测,预测结果的平均绝对误差由0.52%降低到了0.29%,证明了演化追踪法的可利用性与有效性。     

Exogenous pressures and probabilistic assessment of risk and vulnerability of urban water systems in Ghana

ABSTRACT

Risk and vulnerability assessment of urban water systems can be extended to include several components. This work formulates a probable quantitative assessment of risk and vulnerability of urban water system based on climatic conditions and urban population growth. Climate change scenarios and population projections are used to estimate susceptibility to water supply systems’ risk and vulnerability. Quantile regression was used to establish the exponent correlation between the climate variables and population; and evaluate their consequential influences on urban water supply systems. We complemented the analysis with a probabilistic model to assess the robustness of urban water system that depends wholly on the climate for freshwater source. The study established that Climatic conditions, though uncertain, point to freshwater deficiency in the future. Moreover, population trends project a higher urban population thereby increasingly lowering water per capita and subsequently leading to doubtful urban water system’s resilience to the exogenous pressures.     

Urban water security - what does it mean?

ABSTRACT

This research is focussed on understanding what urban water security means – a surprisingly elusive concept given the global shift from rural to urban living. We first make the case for a distinct urban water security definition. We then identify 25 unique water security definitions, of which three relate to the urban context but all with scope for improvement. Applying novel indices, we assess the prevalence, complexity and evolution of themes and dimensions within all definitions and find a stable spectrum of themes; but note a shifting emphasis towards environmental and social dimensions, away from quality and quantity of supply. Overall the definitions are becoming more comprehensive by simply listing more outcomes to be achieved. Instead of this ‘shopping-list’ approach, we propose a simplified urban water security definition with a focus on agreement of needs with community stakeholders, while using the themes to guide what the objectives might be.     

Soft computing for solar radiation potential assessment in Algeria

ABSTRACT

Precise estimation of solar radiation is a highly required parameter for the design and assessment of solar energy applications. Over the past years, many machine learning techniques have been proposed in order to improve the forecasting performance using different input attributes. The aim of this study is the forecasting of one day ahead of horizontal global solar radiation using a set of meteorological and geographical inputs. In this respect, the Gaussian process regression methodology (GPR) and least-square support vector machine (LS-SVM) with different kernels are evaluated in order to select the most appropriate forecasting model. In order to assess the proposed models, the southern Algerian city, Ghardaia regions, was selected for this study. A historical data of five years (2013–2017) of meteorological data collected at Renewable Energies (URAER) in Ghardaia city are used. The achieved results demonstrate that all the proposed models give approximately similar results in terms of statistical indicators. In term of processing time, all the models showed acceptable computational efficiency with less computational costs of the GPR model among all machine learning models.     

Using extreme learning machines for short-term urban water demand forecasting

This study explores the ability of various machine learning methods to improve the accuracy of urban water demand forecasting for the city of Montreal (Canada). Artificial Neural Network (ANN), Support Vector Regression (SVR) and Extreme Learning Machine (ELM) models, in addition to a traditional model (Multiple linear regression, MLR) were developed to forecast urban water demand at lead times of 1 and 3 days. The use of models based on ELM in water demand forecasting has not previously been explored in much detail. Models were based on different combinations of the main input variables (e.g., daily maximum temperature, daily total precipitation and daily water demand), for which data were available for Montreal, Canada between 1999 and 2010. Based on the squared coefficient of determination, the root mean square error and an examination of the residuals, ELM models provided greater accuracy than MLR, ANN or SVR models in forecasting Montreal urban water demand for 1 day and 3 days ahead, and can be considered a promising method for short-term urban water demand forecasting.     

基于小波支持向量机的城市用水量非线性组合预测

基于支持向量机(SVM)和小波框架理论,建立了城市用水量非线性组合预测模型,介绍了该模型的结构设计方法,并采用SMO算法对模型进行求解。实例表明,该模型具有很强的泛化能力与适应数据和函数变化的能力,能够有效提高预测精度,可用于供水系统调度的用水量预测。     

Prioritization of development interventions in Egyptian slums through an urban appraisal index

ABSTRACT

The evident conflict in upgrading policies between several entities necessitates the crucial need to develop a reliable urban appraisal method to ensure successful interventions. The Egyptian initiative, aims at categorizing them into ‘unsafe’ versus ‘unplanned’ areas. Yet, it has ignored some basic socio-spatial and psychological quality of life indicators. Accordingly, the study aims at strengthening the local-level data collection through testing the reliability of the ‘ISDF’ index versus the two other sets of indicators statistically, by conducting an urban socio-economic survey on 30 different Ashwa’iyyat areas in Egypt.     

A composite indicator for water meter replacement in an urban distribution network

In water supply management, volumetric water meter are typically used to measure users' consumption. With water meters, utilities can collect useful data for billing, assess the water balance of the system, and identify failures in the network, water theft and anomalous user behaviour. Despite their importance, these instruments are characterised by intrinsic errors that cause so-called apparent losses. The complexity of the physical phenomena associated with metering errors in aging water meters does not allow meter replacement to be guided by single parameters, such as the meter age or the total volume passed through the meter. This paper presents a meter replacement strategy based on a composite ‘Replacement Indicator’ (RI) that aims to reduce apparent losses. The performance of a meter during its operating life was analysed by means of this indicator, which signals when the meter needs to be replaced. To test the reliability and robustness of the proposed indicator, a Monte Carlo uncertainty analysis was performed. The methodology was applied to a real case study: a district metered area (DMA) in the Palermo city water distribution network (Italy). The analysis showed that ranking based on the composite indicator is better than common ranking procedures based on typical variables (e.g., the meter error curve or the meter age): the proposed indicator can better select the meters to be replaced and favourably affect the associated costs.     

Random Process Model for Urban Traffic Flow Using a Wavelet‐Bayesian Hierarchical Technique

Abstract: The existing well‐known short‐term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to predict the future traffic conditions. This article proposes a random process traffic volume model that enables estimation and prediction of traffic volume at sites where such large and continuous data sets of traffic condition related information are unavailable. The proposed model is based on a combination of wavelet analysis (WA) and Bayesian hierarchical methodology (BHM). The average daily “trend” of urban traffic flow observations can be reliably modeled using discrete WA. The remaining fluctuating parts of the traffic volume observations are modeled using BHM. This BHM modeling considers that the variance of the urban traffic flow observations from an intersection vary with the time‐of‐the‐day. A case study has been performed at two busy junctions at the city‐centre of Dublin to validate the effectiveness of the strategy.     

The evolution of urban water systems: societal needs,institutional complexities,and resource costs

ABSTRACT

Urban water systems provide critical services to meet the supply, sanitation, and drainage needs of urban societies. Evolving needs have resulted in increasingly expansive infrastructure, raising questions about the sustainability of such large infrastructure investments. In this study, we demonstrate the historical interplay between growing urban water needs, the services developed to meet them, and their total resource cost. We hypothesize that needs evolve hierarchically, with predictable outcomes in the form of service progression. To test this hypothesis, we use a suite of metrics at the US national scale indicative of our proposed hierarchy levels. At the city scale, we assess the cost implications of this progression of services. We use the emergy framework to quantitatively reconstruct the historical resource requirements of supply, sanitation, and drainage services and show how evolving needs lead to mounting resource costs. Lastly, we discuss implications of continually increasing complexity for meeting future water needs.     

The Point is to Change It

ABSTRACT

This paper explores how planning practices contribute to the reification of the ‘state’ through the case of Singapore’s new urban waterfront, Marina Bay. Instead of assuming Singapore’s state-led planning model as inherently ‘top-down’ and ‘long-term’, it disaggregates the planning process into three specific modes of abstraction – calculation, historicity and imagination – and analyzes the role of each in reifying the ‘state’ as the singular author of history and development. The case contributes to the literature by illuminating how ‘states’ can appear to have different forms, spatialities, agencies and ultimately consequences, without compartmentalizing planning models based primarily on ideological or geopolitical divisions.     

Assessing the influence of exogenous and quality of service variables on water companies´ performance using a true-fixed stochastic frontier approach

ABSTRACT

Improving the cost efficiency and quality of service across time corresponds to two important targets for regulated water companies. Following a pioneering approach, we have evaluated the influence of a set of exogenous and quality of service variables on the English and Welsh water companies’ costs over the period of 1996–2009. A true-fixed effects stochastic cost frontier model was applied since it also allowed computing of the productivity change of the water companies and their drivers. Results have shown that quality of service has an influence on water companies´ costs. This issue is very relevant for bursts in water mains since investments in reducing them will lead to a reduction in the water supply interruptions and water losses. Results of this study illustrate the importance of developing long-term planning investment programmes in the water industry.     

Why an urban population continues to grow under intensifying water scarcity: an answer from generalized water resources

ABSTRACT

Previous studies indicated that the population size would arrive at its upper limits under the intense water scarcity in Urumqi. However, these ‘limits’ have been repeatedly exceeded, and the population has grown from one million in the 1980s to four million in 2014. This phenomenon is viewed as a ‘mystery of the population carrying capacity of water resources’. This paper investigates the mystery under an amplified framework of generalized water. The entropy of virtual water was 48.9, and 3.08 billion m³ of external virtual water flowed into Urumqi. External virtual water makes a main contribution to increase water supplies under acute water scarcity, and this contribution results from the characteristic of dissipative structure in an urban economy. It is proved that generalized water is one of the most essential answers to the mystery at least. Generalized water plays an important role and generalized water management is necessary in an urban economy.     

气候变化给城镇给水排水系统带来的挑战

简要介绍了国际和中国对"气候变化与水"的评估和预测结果,论述了当前有关国家和国际组织研究讨论关于城市给水排水发展应对气候变化的相关主要问题,分析了气候变化给中国城市给水排水系统带来的挑战,并提出了相应建议。