Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models

Al-Zahrani, Muhammad A.; Abo-Monasar, Amin

doi:10.1007/s11269-015-1021-z

Cited by 112 publications

(43 citation statements)

References 21 publications

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“…The majority of recent research uses data-driven techniques with learning algorithms for predictive analysis. Artificial neural networks (ANN) are probably the most popular ones [17][18][19], also most studies are done with slight changes in such models when pre-processing the data or changes in the structures of the defined ANN models. One study combined ANN with the wavelet bootstrapping machine learning approach as a hybrid model to improve performance of the models by pre-processing the data [20].…”

Section: Introductionmentioning

confidence: 99%

Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

Shabani

Candelieri

Archetti

et al. 2018

Water

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This article proposes a new general approach in short-term water demand forecasting based on a two-stage learning process that couples time-series clustering with gene expression programming (GEP). The approach was tested on the real life water demand data of the city of Milan, in Italy. Moreover, multi-scale modeling using a series of head-time was deployed to investigate the optimum temporal resolution under study. Multi-scale modeling was performed based on rearranging hourly based patterns of water demand into 3, 6, 12, and 24 h lead times. Results showed that GEP should receive more attention among the emerging nonlinear modelling techniques if coupled with unsupervised learning algorithms in detailed spherical k-means.

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Section: Introductionmentioning

confidence: 99%

Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

Shabani

Candelieri

Archetti

et al. 2018

Water

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“…The basic idea of an ANN is that the network learns from the input data and the associated output data with the help of training algorithms and transfer functions [3]- [6]. Back propagation training algorithm is a supervised learning method based on the gradient descent of the quadratic error function and is considered as the universal function approximator [4], [5]. During the learning process, the gradient descent method is used to minimize the total error or mean error of the output computed by the network [1], [6].…”

Section: Introductionmentioning

confidence: 99%

“…ANN modelling approaches have been embraced enthusiastically by practitioners in water resources, as they are perceived to overcome some of the difficulties associated with traditional statistical approaches [1], [4], [7]. With the changing landscape and climate brought about by weather phenomena and unprecedented human activities, water as a very important environmental resource should be managed scientifically with the use of tools and techniques that will optimize usage management and conservation.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Artificial Neural Networks Training Algorithms and Transfer Functions for Medium-Term Water Consumption Forecasting

Velasco¹,

Granados²,

Ortega³

et al. 2018

ijacsa

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“…The time step used typically ranges from a day [11,18] to an hour [12,19], or to as little as a quarter of an hour in the case of the model of [13]. There are also models with multiperiodicity, in which water demand is forecast at different time steps, for example on a daily and hourly basis, as in [10].…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Water Demand Forecasting Model Using a Moving Window on Previously Observed Data

Pacchin

Alvisi

Franchini

2017

Water

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Abstract:In this article, a model for forecasting water demands over a 24-h time window using solely a pair of coefficients whose value is updated at every forecasting step is presented. The first coefficient expresses the ratio between the average water demand over the 24 h that follow the time the forecast is made and the average water demand over the 24 h that precede it. The second coefficient expresses the relationship between the average water demand in a generic hour falling within the 24-h forecasting period and the average water demand over that period. These coefficients are estimated using the information available in the weeks prior to the time of forecasting and, therefore, the model does not require any actual calibration process. The length of the time series necessary to implement the model is thus just a few weeks (3-4 weeks) and the model can be parameterized and used without there being any need to collect hourly water demand data for long periods. The application of the model to a real-life case and a comparison with results provided by another model already proposed in the scientific literature show it to be effective, robust, and easy to use.

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Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models

Cited by 112 publications

References 21 publications

Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

Performance Analysis of Artificial Neural Networks Training Algorithms and Transfer Functions for Medium-Term Water Consumption Forecasting

A Short-Term Water Demand Forecasting Model Using a Moving Window on Previously Observed Data

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