Water Demand Forecasting Model for the Metropolitan Area of São Paulo, Brazil

Santos, Cláudia Cristina dos; Filho, Augusto José Pereira

doi:10.1007/s11269-014-0743-7

Cited by 39 publications

(34 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Climatic variables can affect short-term and mid-term values while socioeconomic variables are useful for long-term forecasting [11,15,16]. However, a few studies investigated the impact of climatic variables on demand forecasting [17][18][19]. Literature enlists various deterministic and probabilistic techniques for forecasting urban drinking water demand.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Wavelet Decomposition and Phase Space Reconstruction in Urban Water Consumption Forecasting: Chaotic Approach (Case Study)

Yousefi¹,

Naser²,

Mohammadi³

2018

Wavelet Theory and Its Applications

View full text Add to dashboard Cite

The forecasting of future value of water consumption in an urban area is highly complex and nonlinear. It often exhibits a high degree of spatial and temporal variability. It is a crucial factor for long-term sustainable management and improvement of the operation of urban water allocation system. This chapter will study the application of two preprocessing phase space reconstruction (PSR) and wavelet decomposition transform (WDT) methods to investigate the behavior of time series to forecast short-term water demand value of Kelowna City (BC, Canada). The research proposes two pre-process technique to improve the accuracy of the models. Artificial neural networks (ANNs), gene expression programming (GEP) and multilinear regression (MLR) methods are the tools that considered for forecasting the demand values. Evaluation of the tools is based on two steps with and without applying the pre-processing methods. Moreover, autocorrelation function (ACF) is used to calculate the lag time. Correlation dimension is used to study the chaotic behavior of the dataset. The models' relative performance is compared using three different fitness indexes; coefficient of determination (CD), root mean square error (RMSE) and mean absolute error (MAE). The results showed how pre-processing combination of WDT and PSR improved the performance of the models in forecasting short-term demand values.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Application of Wavelet Decomposition and Phase Space Reconstruction in Urban Water Consumption Forecasting: Chaotic Approach (Case Study)

Yousefi¹,

Naser²,

Mohammadi³

2018

Wavelet Theory and Its Applications

View full text Add to dashboard Cite

show abstract

“…Processes are considered as components of a whole, complete and integrated water cycle [14]. According to [15], the primary objective of the UWS analyses should be first to balance out demand with supply. Therefore, the water demand forecast has been developed mainly to understand spatial and temporal patterns of water use in the future [16], as well as for better management of water resources [17].…”

Section: Urban Water System Modelling Approachmentioning

confidence: 99%

Mitigation Options for Future Water Scarcity: A Case Study in Santa Cruz Island (Galapagos Archipelago)

Reyes

Trifunović

Sharma

et al. 2017

Water

View full text Add to dashboard Cite

Santa Cruz Island (Galápagos Archipelago), like many other tourist islands, is currently experiencing an exponential increase in tourism and local population growth, jeopardizing current and future water supply. An accurate assessment of the future water supply/demand balance is crucial to capital investment for water infrastructure. This paper aims to present five intervention strategies, which are suggested to solve the future water crisis. The strategies combined include environmentally sustainable options such as rainwater harvesting, greywater recycling and water demand management, as well as desalination. These strategies were evaluated under four population growth scenarios (very fast, fast, moderate and slow growths) by using several Key Performance Indicators (KPI's) including water demand, leakage levels, total costs, energy consumption, rainwater delivered and greywater recycled. Moreover, it also aims to develop a methodology for similar islands, using the WaterMet 2 modelling approach, a tool for integrated of sustainable-based performance of urban water systems. The results obtained show that by 2044 only a small portion of the future water demand can be covered assuming business as usual. Therefore, desalination seems to be the most viable option in order to mitigate the lack of water at the end of the planning period considering the growth trends. However, strategies comprising more environmentally friendly alternatives may be sufficient, but only under slow population growth scenarios.

show abstract

“…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%

“…However, in some cases, climate factors such as precipitation, temperature, and humidity are also considered, as they can have a significant impact on water demand [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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

Pacchin

Alvisi

Franchini

2017

Water

View full text Add to dashboard Cite

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.

show abstract

Water Demand Forecasting Model for the Metropolitan Area of São Paulo, Brazil

Cited by 39 publications

References 13 publications

Application of Wavelet Decomposition and Phase Space Reconstruction in Urban Water Consumption Forecasting: Chaotic Approach (Case Study)

Application of Wavelet Decomposition and Phase Space Reconstruction in Urban Water Consumption Forecasting: Chaotic Approach (Case Study)

Mitigation Options for Future Water Scarcity: A Case Study in Santa Cruz Island (Galapagos Archipelago)

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

Contact Info

Product

Resources

About