Stochastic Model to Evaluate Residential Water Demands

García, Vicente J. Serradell; García-Bartual, Rafael; Cabrera, Enrique; Arregui, Francisco J.; García-Serra, Jorge

doi:10.1061/(asce)0733-9496(2004)130:5(386)

Cited by 77 publications

(33 citation statements)

References 7 publications

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“…This distribution is suitable for random, identically distributed, and independent variables and is widely used to characterize extreme regimes of oceanographic geophysical variables. Gumbel's maximum distribution is a bi-parametric function with a location parameter λ and a scale parameter δ (García et al, 2004):…”

Section: Methodsmentioning

confidence: 99%

“…Although there is no physical, theoretical, or empirical evidence of what the selection or adjustment of a probability distribution function should be for the calculation of the design wave height, the Gumbel or Fisher Tippett I and Weibull distribution functions are widely used for such purposes, as reported by Martínez and Coria (1993), Katz et al (2002), García et al (2004), andNaveau et al (2005).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Storms or cold fronts: what is really responsible for the extreme waves regime in the Colombian Caribbean coastal region?

Díaz

Royero

Ruiz-Merchan

et al. 2016

Nat. Hazards Earth Syst. Sci.

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Abstract. The aim of this study is to determine the contribution and importance of cold fronts and storms to extreme waves in different areas of the Colombian Caribbean in an attempt to determine the extent of the threat posed by the flood processes to which these coastal populations are exposed. Furthermore, the study wishes to establish the actions to which coastal engineering constructions should be subject. In the calculation of maritime constructions, the most important parameter is the height of the wave. For this reason, it is necessary to establish the design wave height to which a coastal engineering structure should be resistant. This wave height varies according to the return period considered. The significant height values for the areas focused on in the study were calculated in accordance with Gumbel's extreme value methodology. The methodology was evaluated using data from the reanalysis of the spectral National Oceanic and Atmospheric Administration (NOAA) WAVEWATCH III® (WW3) model for 15 points along the 1600 km of the Colombian Caribbean coastline (continental and insular) between the years 1979 and 2009. The results demonstrated that the extreme waves caused by tropical cyclones and those caused by cold fronts have different effects along the Colombian Caribbean coast. Storms and hurricanes are of greater importance in the Guajira Peninsula (Alta Guajira). In the central area (consisting of Baja Guajira, and the cities of Santa Marta, Barranquilla, and Cartagena), the strong impact of cold fronts on extreme waves is evident. However, in the southern region of the Colombian Caribbean coast (ranging from the Gulf of Morrosquillo to the Gulf of Urabá), the extreme values of wave heights are lower than in the previously mentioned regions, despite being dominated mainly by the passage of cold fronts. Extreme waves in the San Andrés and Providencia insular region present a different dynamic from that in the continental area due to their geographic location. The wave heights in the extreme regime are similar in magnitude to those found in Alta Guajira, but the extreme waves associated with the passage of cold fronts in this region have lower return periods than those associated with the hurricane season.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Storms or cold fronts: what is really responsible for the extreme waves regime in the Colombian Caribbean coastal region?

Díaz

Royero

Ruiz-Merchan

et al. 2016

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Therefore, the single-use events obtained from the automatic disaggregation algorithms developed in this study are significantly more reliable, in terms of duration, average flow rate and shape than those resulting from a manual processing and cropping the water consumption flow traces. These results have direct implications in the probability functions used to characterize water consumption events frequency, duration and intensity [30][31][32][33][34]. Obviously, more accurate classification techniques can be developed as processing experience is gained and larger and more reliable data sets are available for training the algorithms.…”

Section: R1mentioning

confidence: 99%

Water End Use Disaggregation Based on Soft Computing Techniques

Pastor-Jabaloyes

Arregui

Cobacho

2018

Water

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Disaggregating residential water end use events through the available commercial tools needs a great investment in time to manually process smart metering data. Therefore, it is extremely difficult to achieve a homogenous and sufficiently large corpus of classified single-use events capable of accurately describe residential water consumption. The main goal of the present paper is to develop an automatic tool that facilitates the disaggregation of the individual water consumptions events from the raw flow trace. The proposed disaggregation methodology is conducted through two actions that are iteratively performed: first, the use of an advanced two-step filter, whose calibration is automatically conducted by the Elitist Non-Dominated Sorting Genetic Algorithm NSGA-II; and second, a cropping algorithm based on the filtered water consumption flow traces. As a secondary goal, yet complementary to the main one, a semiautomatic massive classification process has been developed, so that the resulting single-use events can be easily categorized in the different water end uses in a household. This methodology was tested using water consumption data from two different case studies. The characteristics of the households taken as reference and their occupants were unequivocally dissimilar from each other. In addition, the monitoring equipment used to obtain the consumption flow traces had completely different technical specifications. The results obtained from the processing of the two studies show that the automatic disaggregation is both robust and accurate, and produces significant time saving compared to the standard manual analysis.

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“…From extensive measurements it is possible to estimate the parameters to constitute a Poisson Rectangular Pulse (PRP) model (Buchberger and Wells, 1996). Measurements were collected in the USA (Ohio; Buchberger et al, 2003), Italy (Guercio et al, 2001), Spain (García et al, 2004) and Mexico (Alcocer-Yamanaka et al, 2006) and for each area the PRP parameters were determined. To estimate intensity and duration different probability distributions are applicable for different data sets, such as log-normal, exponential and Weibull distributions.…”

Section: Demand Modellingmentioning

confidence: 99%

Importance of demand modelling in network water quality models: a review

Blokker

Vreeburg

Buchberger

et al. 2008

Drink. Water Eng. Sci.

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Abstract. Today, there is a growing interest in network water quality modelling. The water quality issues of interest relate to both dissolved and particulate substances. For dissolved substances the main interest is in residual chlorine and (microbiological) contaminant propagation; for particulate substances it is in sediment leading to discolouration. There is a strong influence of flows and velocities on transport, mixing, production and decay of these substances in the network. This imposes a different approach to demand modelling which is reviewed in this article.For the large diameter lines that comprise the transport portion of a typical municipal pipe system, a skeletonised network model with a top-down approach of demand pattern allocation, a hydraulic time step of 1 h, and a pure advection-reaction water quality model will usually suffice. For the smaller diameter lines that comprise the distribution portion of a municipal pipe system, an all-pipes network model with a bottom-up approach of demand pattern allocation, a hydraulic time step of 1 min or less, and a water quality model that considers dispersion and transients may be needed.Demand models that provide stochastic residential demands per individual home and on a one-second time scale are available. A stochastic demands based network water quality model needs to be developed and validated with field measurements. Such a model will be probabilistic in nature and will offer a new perspective for assessing water quality in the drinking water distribution system.

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Stochastic Model to Evaluate Residential Water Demands

Cited by 77 publications

References 7 publications

Storms or cold fronts: what is really responsible for the extreme waves regime in the Colombian Caribbean coastal region?

Storms or cold fronts: what is really responsible for the extreme waves regime in the Colombian Caribbean coastal region?

Water End Use Disaggregation Based on Soft Computing Techniques

Importance of demand modelling in network water quality models: a review

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