Prediction of urban water demand on the basis of Engel's coefficient and Hoffmann index: case studies in Beijing and Jinan, China

Domestic and industrial water uses are the most important segment of urban water consumption. Traditional urban water demand models are usually based on water consumption quotas or statistical relationships, which usually overestimate urban water demands. The efficiency of domestic and industrial water uses is associated with living standards and levels of industrialization. The correlation coefficient between per capita water consumption and Engel's Coefficient in Beijing and Jinan is 0.62 and 0.53, respectively. These values are much smaller than the correlation between added industrial value and the Hoffmann Index in Beijing (0.95) and Jinan (0.90). Demand models for urban water consumption, including a domestic water demand model based on Engel's Coefficient and an industrial water demand model based on the Hoffmann Index, were developed in this study to predict urban water demand in Beijing and Jinan for 2020. The results show that the models can effectively capture the trends of urban water demand. Urban water consumption in these two cities from 1995 to 2007 was used to calibrate the models. The coefficients of determination for residential and industrial water uses were 0.93 and 0.68 in Beijing, and 0.79 and 0.64 in Jinan. Social, economic and climate scenarios for Beijing and Jinan in 2020 were generated according to the Urban Master Plans for these two cities, and they formed the basis for predictions of water consumption in 2020. The results show that total water consumption will increase by 67.6% in Jinan and 33.0% in Beijing when compared with consumption from 2007.

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The use of band similarity in urban water demand forecasting as a new method

Water Science & Technology Water Supply ◽

10.2166/ws.2021.221 ◽

2021 ◽

Author(s):

V. Yılmaz

Keyword(s):

Water Consumption ◽

Optimization Algorithm ◽

Water Demand ◽

Demand Forecasting ◽

New Method ◽

Urban Water ◽

Artificial Bee Colony Optimization ◽

Urban Water Demand ◽

Demand Models ◽

Similarity Method

Abstract Water consumptions and demands by persons vary from time to time and from location to location depending on countless factors, notably, population, socio-economic and climatic variables. Today, studies which create models on water consumption of persons, using numerous methods including artificial neural networks and regression models in this regard and ensure that projections are made are ongoing. In this study; parameters affecting water consumption were examined within the scope of the study area, and the parameter reduction was realized with the help of the Factor Analysis. Then, as a new method, the Band Similarity method was used together with the Artificial Bee Colony optimization algorithm, and urban water demand models were produced and the temporal dependence of the relevant variables was examined. As a result of the study, it was seen that the Band Similarity method improved the results obtained with the optimization algorithm and helped to understand the temporal dependencies of the variables. The fact that the Band Similarity method, which was put forward for the first time in its field, worked successfully and produced results, can be said to be the main contribution of this study to the knowledge.

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Forecasting Urban Water Demand Using Cellular Automata

Water ◽

10.3390/w12072038 ◽

2020 ◽

Vol 12 (7) ◽

pp. 2038

Author(s):

Laís Marques de Oliveira ◽

Samíria Maria Oliveira da Silva ◽

Francisco de Assis de Souza Filho ◽

Taís Maria Nunes Carvalho ◽

Renata Locarno Frota

Keyword(s):

Cellular Automata ◽

Water Consumption ◽

Water Demand ◽

Water Resource Management ◽

Transition Probability ◽

Management Strategies ◽

Weight Of Evidence ◽

Annual Growth Rate ◽

Urban Water ◽

Urban Water Demand

Associating the dynamic spatial modeling based on the theory of cellular automata with remote sensing and geoprocessing technologies, this article analyzes what would be the per capita consumption behavior of Fortaleza-CE, located in the Northeast of Brazil, in 2017, had there not been a period of water scarcity between 2013 and 2017, and estimates the future urban water demand for the years 2021 and 2025. The weight of evidence method was applied to produce a transition probability map, that shows which areas will be more subject to consumption class change. For that, micro-measured water consumption data from 2009 and 2013 were used. The model was validated by the evaluation of diffuse similarity indices. A high level of similarity was found between the simulated and observed data (0.99). Future scenarios indicated an increase in water demand of 6.45% and 10.16% for 2021 and 2025, respectively, compared to 2017. The simulated annual growth rate was 1.27%. The expected results of urban water consumption for the years 2021 and 2025 are essential for local water resources management professionals and scientists, because, based on our results, these professionals will be able to outline future water resource management strategies.

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Water demand forecasting using extreme learning machines

Journal of Water and Land Development ◽

10.1515/jwld-2016-0004 ◽

2016 ◽

Vol 28 (1) ◽

pp. 37-52 ◽

Cited By ~ 14

Author(s):

Mukesh Tiwari ◽

Jan Adamowski ◽

Kazimierz Adamowski

Keyword(s):

Water Demand ◽

Demand Forecasting ◽

Urban Water ◽

Demand Model ◽

Demand Prediction ◽

Learning Machines ◽

Water Demand Forecasting ◽

Urban Water Demand ◽

Learning Machine ◽

Similar Accuracy

AbstractThe capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELMW, ELMB), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANNW, ANNB). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELMBand ANNBmodels provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANNWand ELMWmodels provided greater accuracy, with the ELMWmodel outperforming the ANNWmodel. Significant improvement in peak urban water demand prediction was only achieved with the ELMWmodel. The superiority of the ELMWmodel over both the ANNWor ANNBmodels demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

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A class of time series urban water demand models with nonlinear climatic effects

Water Resources Research ◽

10.1029/wr026i002p00169 ◽

1990 ◽

Vol 26 (2) ◽

pp. 169-178 ◽

Cited By ~ 50

Author(s):

Shaw-Pin Miaou

Keyword(s):

Time Series ◽

Water Demand ◽

Urban Water ◽

Climatic Effects ◽

Urban Water Demand ◽

Demand Models

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A hybrid model based on CNN and Bi-LSTM for urban water demand prediction

2019 IEEE Congress on Evolutionary Computation (CEC) ◽

10.1109/cec.2019.8790060 ◽

2019 ◽

Cited By ~ 6

Author(s):

Piao Hu ◽

Jun Tong ◽

Jingcheng Wang ◽

Yue Yang ◽

Luca de Oliveira Turci

Keyword(s):

Hybrid Model ◽

Water Demand ◽

Urban Water ◽

Demand Prediction ◽

Urban Water Demand ◽

Model Based ◽

Water Demand Prediction

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Urban Comprehensive Water Consumption: Nonlinear Control of Production Factor Input Based upon the C-D Function

Sustainability ◽

10.3390/su11041125 ◽

2019 ◽

Vol 11 (4) ◽

pp. 1125 ◽

Cited By ~ 1

Author(s):

Kebai Li ◽

Tianyi Ma ◽

Tom Dooling ◽

Guo Wei

Keyword(s):

Water Supply ◽

Production Function ◽

Water Consumption ◽

Water Demand ◽

Demand Function ◽

Production Factor ◽

Urban Water ◽

Urban Water Supply ◽

Labor Input ◽

Urban Water Demand

Utilizing the urban water demand function and the Cobb-Douglas (C-D) production function, an economic control model for the multi-input-multi-output (MIMO) nonlinear system was designed and implemented to describe urban comprehensive water consumption, where the urban water demand function was expressed as the product of the number of water users and per capita comprehensive water consumption, and the urban water supply function was expressed as a C-D production function. The control variables included capital investment and labor input for the urban water supply. In contrast to the Solow model, Shell model and aggregate model with renewable labor resources, the proposed model eliminated value constraints on investment and labor input in the state equations and hence avoided the difficulty in applying these models to urban water supply institutions. Furthermore, the feedback linearization control design (FLCD) method was employed to accomplish stability of the system. In contrast to the optimal control method, the FLCD method possesses an explicit solution of the control law and does not require the solution of a two-point boundary value problem of an ordinary differential equation, making the method more convenient for application. Moreover, two different scenarios of urban water consumption, one for the growth period and the other for the decline period, were simulated to demonstrate the effectiveness of the proposed control scheme.

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