Influence of a season on hourly and daily variations in water demand patterns in a rural water supply line – case study

Bergel, T.; Szeląg, Bartosz; Woyciechowska, Olga

doi:10.1515/jwld-2017-0038

Cited by 9 publications

(8 citation statements)

References 5 publications

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“…However, the IW (≈37%) of the negative outlier network for this input feature was not expected and aligns with the fact that some of the days with high temperatures result in under-consumption compared to the baseline. A similar observation was made in [9] where fluctuations during spring and summer were found to be more pronounced than during the autumn and winter.…”

Section: Discussionsupporting

confidence: 81%

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Differential Learning for Outliers: A Case Study of Water Demand Prediction

Shah

Miled

Schaefer³

et al. 2018

Applied Sciences

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Predicting water demands is becoming increasingly critical because of the scarcity of this natural resource. In fact, the subject was the focus of numerous studies by a large number of researchers around the world. Several models have been proposed that are able to predict water demands using both statistical and machine learning techniques. These models have successfully identified features that can impact water demand trends for rural and metropolitan areas. However, while the above models, including recurrent network models proposed by the authors are able to predict normal water demands, most have difficulty estimating potential deviations from the norms. Outliers in water demand can be due to various reasons including high temperatures and voluntary or mandatory consumption restrictions by the water utility companies. Estimating these deviations is necessary, especially for water utility companies with a small service footprint, in order to efficiently plan water distribution. This paper proposes a differential learning model that can help model both over-consumption and under-consumption. The proposed differential model builds on a previously proposed recurrent neural network model that was successfully used to predict water demand in central Indiana.

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Section: Discussionsupporting

confidence: 81%

“…In [3,4], linear regression models were used. Seasonal variations in hourly and daily water demand patterns are investigated in [8,9]. In [8], time-series clustering and support vector regression (SVR) were used for hourly water demand prediction in Milan, Italy.…”

Section: Related Workmentioning

confidence: 99%

Differential Learning for Outliers: A Case Study of Water Demand Prediction

Shah

Miled

Schaefer³

et al. 2018

Applied Sciences

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“…This may indicate seasonal variations in the sewage outflow. It is postulated that for the specific sewage system, seasonal variations are mainly caused by diversified water demand, with reference to different time periods during the year or the type of the area from which sewage is discharged: tourist, rural, suburban, or urban [41][42][43][44]. Despite the existence of some real autocorrelations, this fact did not influence the conclusions carried out by the MMK test, as evidenced by the values of the modified Z* statistic and the modified p* value.…”

Section: Resultsmentioning

confidence: 99%

An Analysis of Seasonal Waste Draining for the Urban Agglomeration Using Statistical Methods

Młyński

Kurek

Bugajski

2018

Water

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The aim of the work was to analyze the seasonality of the sewage outflow from the urban agglomeration of Radom, using statistical methods in the aspect of environmental protection. The research was carried out on the basis of the observational series covering the daily volume of the sewage outflow in the years 2013–2015. The assessment was carried out according to the following stages: identification of the distribution of the average daily sewage outflow in particular months using nuclear estimators, seasonal evaluation using the Colwell indicators and Fourier spectral analysis, and identification of homogeneous seasons with regard to the sewage outflow using the cluster analysis. On the basis of the calculations that were carried out, no significant seasonality of the sewage outflow was noted, which results from the separated character of the sewage system in the urban agglomeration of Radom. The analyses showed that the applied statistical techniques are a practical solution for identifying the seasonality of sewage inflow to the treatment plant, thanks to which it will be possible to take appropriate actions related to minimizing the harmful impact of hydraulic overload on the biotic environment of the natural receiver.

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“…Furthermore, formulation also differs based on seasonal factors and conditions, depending on freshwater needs both for domestic and non-domestic use. Therefore, in calculation and planning, assumptions or approaches based on the classification of seasonal characteristics within a city are needed (Bergel et al 2017). Calculation of urban water needs can be carried out according to the estimation presented in Table 1.…”

Section: Water Needs Measurementsmentioning

confidence: 99%

“…Meanwhile, maximum water hour requirement (Qpeak) requires reference to fluctuations in the maximum time requirement (fpeak) in its calculation. Thus, the largest amount of water required at certain hours can be calculated (Bergel et al 2017). In addition to the maximum daily water requirements and maximum water hour requirements, water loss is also one of the determinants of average water requirement.…”

Section: Fluctuations Of Water Needsmentioning

confidence: 99%

Towards a sustainable city: planning the fulfilment of urban fresh water needs of Depok in both dry and rainy seasons

Herdiansyah

Putra

2018

Water Practice and Technology

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Population growth over recent years in Depok has increased demand for fresh water for daily necessities. These necessities include the fresh water required by both settlements and commercial operations. The purpose of this study is to analyse the demand for fresh water created by the population growth of Depok in realising its development as a sustainable city. This study uses a quantitative approach presented through narrative analysis. In 2016, the average water requirement of Depok's population of 2,180,000 people was 3,070 litres/second in the dry season and 5,983 litres/second in the rainy season. Population projections for 2030 suggest that the population of Depok will have increased to 3,543,101 people, and that the average water requirement will then be 4,990 litres/second in the dry season and 9,724 litres/second in the rainy season. Planning alternatives proposed to meet these needs include improving the evaluation of freshwater production capacity and developing freshwater supply systems. Thus, it expected that the projected freshwater needs of Depok in 2030 can be fulfilled. This expectation can be managed and achieved through participation by society and government. However, obstacles are still faced, mainly due to more recent cultural attitudes towards using water.

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Influence of a season on hourly and daily variations in water demand patterns in a rural water supply line – case study

Cited by 9 publications

References 5 publications

Differential Learning for Outliers: A Case Study of Water Demand Prediction

Differential Learning for Outliers: A Case Study of Water Demand Prediction

An Analysis of Seasonal Waste Draining for the Urban Agglomeration Using Statistical Methods

Towards a sustainable city: planning the fulfilment of urban fresh water needs of Depok in both dry and rainy seasons

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