Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction

Anele, Amos O.; Hamam, Yskandar; Abu‐Mahfouz, Adnan M.; Todini, E.

doi:10.3390/w9110887

Cited by 53 publications

(43 citation statements)

References 46 publications

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“…It is based upon the past water use, and socio-economic and climate parameters associated with the past water use. These parameters are precipitation (rain), temperature, seasonality, and evapotranspiration, water price, income, family size and other related factors [113][114][115][116]. Short-term water demand forecasting is essential for operation and management of networks whereas long-term forecasting is required by utility managers for planning and design of WDNs.…”

Section: Demand Forecast Modelsmentioning

confidence: 99%

Solving Management Problems in Water Distribution Networks: A Survey of Approaches and Mathematical Models

Bello

Abu‐Mahfouz

Hamam

et al. 2019

Water

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Modern water distribution networks (WDNs) are complex and difficult to manage due to increased level of urbanization, varying consumer demands, ageing infrastructure, operational costs, and inadequate water resources. The management problems in such complex networks may be classified into short-term, medium-term, and long-term, depending on the duration at which the problems are solved or considered. To address the management problems associated with WDNs, mathematical models facilitate analysis and improvement of the performance of water infrastructure at minimum operational cost, and have been used by researchers, water utility managers, and operators. This paper presents a detailed review of the management problems and essential mathematical models that are used to address these problems at various phases of WDNs. In addition, it also discusses the main approaches to address these management problems to meet customer demands at the required pressure in terms of adequate water quantity and quality. Key challenges that are associated with the management of WDNs are discussed. Also, new directions for future research studies are suggested to enable water utility managers and researchers to improve the performance of water distribution networks.

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Section: Demand Forecast Modelsmentioning

confidence: 99%

Solving Management Problems in Water Distribution Networks: A Survey of Approaches and Mathematical Models

Bello

Abu‐Mahfouz

Hamam

et al. 2019

Water

Self Cite

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“…The MCP is a Bayesian method (i.e., uncertainty post-processor) used to estimate the predictive uncertainty, which is conditional on a set of predictions by one or more deterministic forecasting models. The MCP is selected based on the recent applications that have proven its validity and robustness [26][27][28][29].…”

Section: Demand Forecastingmentioning

confidence: 99%

Real-Time Dynamic Hydraulic Model of Water Distribution Networks

Abu‐Mahfouz

Hamam

Page

et al. 2019

Water

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The impact of climate change and increasing urbanisation throughout the world has forced water utility managers to increase the efficiency of water resources. Reduction of real (or physical) water losses plays a crucial role in improving the efficiency of water supply systems. Considering these challenges, it will not be enough to rely only on traditional approaches to solve the problem of water losses. Therefore, more advanced techniques need to be developed and utilized. Recently, a framework for a real-time dynamic hydraulic model for potable water loss reduction was proposed. This paper focuses mainly on the three major components of the proposed real-time dynamic hydraulic model framework for potable water loss reduction, which have been developed recently. These are background leakage detection, pressure management, and water demand forecasting. A background leakage detection algorithm was proposed which, amongst others, permits the localisation of potential critical nodes or pipes with higher leakage flow in the network where such pressure management could be performed. More so, new controllers (algorithms) which perform pressure management by accurately setting the pressure, using either a pressure control valve or variable speed pump, have been constructed. In addition, background leakage flow is greatly affected by demand variations, a water demand forecasting model is constructed with the aim of annexing the demand variation for multi-period leakage analysis. Thus, a short-term water demand forecast utilising the Model Conditional Processor was constructed to forecast the following hour demand and the associated predictive uncertainty. Although each of these components have been tested independently, future work is ongoing for merging these components and integration within the dynamic hydraulic model framework.

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“…The work in [28] compared the performance of different models for water demand prediction, namely times series models, a feed-forward back-propagation neural network, and a hybrid model. These models were compared pairwise by using the Akaike information criterion that provided an estimation of their quality for short-term predictions.…”

Section: Related Workmentioning

confidence: 99%

A Short-Term Data Based Water Consumption Prediction Approach

et al. 2019

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A smart water network consists of a large number of devices that measure a wide range of parameters present in distribution networks in an automatic and continuous way. Among these data, you can find the flow, pressure, or totalizer measurements that, when processed with appropriate algorithms, allow for leakage detection at an early stage. These algorithms are mainly based on water demand forecasting. Different approaches for the prediction of water demand are available in the literature. Although they present successful results at different levels, they have two main drawbacks: the inclusion of several seasonalities is quite cumbersome, and the fitting horizons are not very large. With the aim of solving these problems, we present the application of pattern similarity-based techniques to the water demand forecasting problem. The use of these techniques removes the need to determine the annual seasonality and, at the same time, extends the horizon of prediction to 24 h. The algorithm has been tested in the context of a real project for the detection and location of leaks at an early stage by means of demand forecasting, and good results were obtained, which are also presented in this paper.

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Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction

Cited by 53 publications

References 46 publications

Solving Management Problems in Water Distribution Networks: A Survey of Approaches and Mathematical Models

Solving Management Problems in Water Distribution Networks: A Survey of Approaches and Mathematical Models

Real-Time Dynamic Hydraulic Model of Water Distribution Networks

A Short-Term Data Based Water Consumption Prediction Approach

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