Prediction of Urban Domestic Water Consumption Considering Uncertainty

Li, Jun; Song, Songbai; Kang, Yan; Wang, Hejia; Wang, Xiaojun

doi:10.1061/(asce)wr.1943-5452.0001329

Cited by 7 publications

(5 citation statements)

References 43 publications

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“…The current state of water resources highlights the need for improved management. It is necessary to recognize [14], measure, and express the value of water and incorporate it into decision making, which is key to achieving sustainable and equitable management of water resources and meeting the United Nations Sustainable Development Goals (SDGs) for 2030 [15].…”

Section: Introductionmentioning

confidence: 99%

The Possibilities of Capturing Rainwater and Reducing the Impact of Floods: A Proposal for the City of Beira, Mozambique

2023

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Modern societies face various challenges, including climate change, rapid urbanization, and sometimes inadequate urban planning policies. In recent years, extreme weather events have received increasing attention for their impacts on cities, humans, and ecosystems worldwide, particularly on coastal towns in Mozambique, such as cyclones, floods, water pollution, and water scarcity, demonstrating their vulnerability to climate change. Cities must adapt to cope with the pressure on their water resources, and it is essential to ensure that communities have access to safe, reliable, and affordable water. A viable way to promote this resilience and simultaneously reduce costs in domestic budgets is to use rainwater to meet daily needs where water quality parameters are not required for consumption. According to the results of this study, it is possible to significantly reduce potable water use from the municipal water supply network by harvesting rainwater, up to 40% when the use does not require potable water at all, proactively protecting this vital resource. In addition to these direct benefits, the large-scale deployment of rainwater harvesting (RWH) systems in densely urbanized areas can also provide indirect benefits, such as reducing peak flow volumes in stormwater drainage systems and potentially reducing the frequency of urban floods. These benefits result from the reduction in the volume and duration of water sent to the drainage network, which can help to improve the overall resilience of communities in the face of climate change and other challenges.

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Section: Introductionmentioning

confidence: 99%

The Possibilities of Capturing Rainwater and Reducing the Impact of Floods: A Proposal for the City of Beira, Mozambique

2023

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“…Their findings suggest that sunshine hours exert a significant influence on UWC, and the BN model's predictive capability is substantially enhanced by incorporating this predictor, especially in the context of a city located in an arid region experiencing rapid population growth. Wang et al [17] utilized annual urban domestic water consumption data from Beijing, Chongqing, and Qingdao to introduce the Kernel Density Estimation-Fractional Order Reverse Accumulative Gray Model. Their results demonstrate that this proposed model outperforms others in predicting the urban domestic water consumption.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Daily Water Consumption in Residential Areas Based on Meteorologic Conditions—Applying Gradient Boosting Regression Tree Algorithm

Li,

Peng,

Zheng

et al. 2023

Water

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A more accurate way of water consumption forecasting can be used to help people develop a scheduling plan of water workers more targeting; therefore, this paper aims to establish a forecast model of daily water consumption based on meteorological conditions. At present, most studies of daily water consumption forecasts focus on historical data or single water use influencing factors; moreover, daily water consumption could be influenced by meteorologic conditions. The influence of complex meteorology factors on water consumption is analyzed based on a gradient-boosted regression tree (GBRT) model. The correlation of 10 meteorologic factors has been discussed and divided into 5 categories, including temperature factor, pressure factor, precipitation factor, sunshine factor, and wind factor. Through the GBRT algorithm, the daily water consumption of residential area could be predicted with a maximum error of ±8%. The results show that the average ground temperature (the feature importance accounts for 81% of the total) has the greatest impact on the daily water consumption of the residential community, followed by the somatosensory temperature (the feature importance accounts for 7% of the total). The method can provide the daily water consumption of water consumption nodes with higher precision for municipal water supply network model accuracy. It also provides a reference for water utility operation schemes and urban development planning.

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“…Currently, the improvement of the grey model is divided into two aspects. One is to extend the order of the grey model from a positive integer number to a positive real number, such as the fractional order forward accumulation model (Wu et al 2013b;Mao et al 2016;Li et al 2021), which can effectively improve the prediction accuracy (Wu et al 2015). The other is to change the forward accumulation to the reverse accumulation to improve the model structure, such as the first-order reverse accumulative model (Che et al 2013;Xiao et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The order of the fractional order reverse accumulative grey model has a great influence on the model prediction effect. In the past, the determination of the order mostly used 'the best fit of historical data' as the objective function to construct the optimization model, and the intelligent optimization algorithm was used to solve the model and obtain the final order (Li et al 2021). This method only took the degree of historical data fitting as the objective function, which was over-fitting and made the model over-learn the noise in the historical data.…”

Section: Introductionmentioning

confidence: 99%

Forecast of regional water demand based on NSGAII-FORAGM

Liu

2021

Water Supply

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Regional water demand is an important basic data for regional engineering planning, design and management. Making full use of multi-source data and prior knowledge to quickly and economically obtain high-precision regional water demand is of great significance to the optimal allocation of regional water resources. In order to accurately predict the regional water demand, this study took Yulin City as a research area to predict the water demand of the city from 2017 to 2019. Aiming at the oscillating characteristics of the regional water demand sequence and the over-fitting problem of traditional prediction models, this study proposed the non-dominated sorting genetic algorithm II-fractional order reverse accumulative grey model (NSGAII-FORAGM). The regional water demand oscillation sequence was transformed into a monotonically decreasing non-negative sequence. Based on the transformation sequence, an optimization model was constructed according to the two objective functions of ‘maximum (or minimum) order’ and ‘best fit to historical data’, and the NSGAII method were adopted to solve the model. The three model structures of ‘fractional order’, ‘reverse accumulation’ and ‘obtaining order through multi-objective optimization model ‘ were tested based on the water use sequence of the three sectors (industry, tertiary industry and domestic) in Yulin City, and the performance of the method is compared with NSGAII-IORAGM, NSGAII-FOFAGM and SOGA-FORAGM. The results showed that the average relative error of the model established in this study for the simulation of industry, tertiary industry (The tertiary industry is a technical name for the service sector of the economy, which encompasses a wide range of businesses), and domestic was 15.54%, 11.20%, 9.98% respectively. The average relative error of the model established in this study for the prediction of industry, tertiary industry and domestic was 9.46%, 7.9%, 1.8% respectively. For the simulation of water demand sequences in three sections, the simulation average relative errors of the other three models were not absolutely dominant except for the SOGA-FORAGM model. The average relative predicted error by the model in this study was the smallest (The relative errors of the three sequence predictions for industry, tertiary industry and domestic were lower than the relative errors of the optimal results of the comparison model, which were 0.97%, 0.72% and 4.5%, respectively), indicating that the model had certain applicability for the water demand prediction of various sectors (industry, tertiary industry and domestic) in the region compared with other models, and can improve the accuracy of the prediction results.

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Prediction of Urban Domestic Water Consumption Considering Uncertainty

Cited by 7 publications

References 43 publications

The Possibilities of Capturing Rainwater and Reducing the Impact of Floods: A Proposal for the City of Beira, Mozambique

The Possibilities of Capturing Rainwater and Reducing the Impact of Floods: A Proposal for the City of Beira, Mozambique

Prediction of Daily Water Consumption in Residential Areas Based on Meteorologic Conditions—Applying Gradient Boosting Regression Tree Algorithm

Forecast of regional water demand based on NSGAII-FORAGM

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