An eco-environmental water demand based model for optimising water resources using hybrid genetic simulated annealing algorithms. Part I. Model development

Wang, Xiaoling; Sun, Yong; Song, Lingguang; Mei, Chuanshu

doi:10.1016/j.jenvman.2009.02.008

Cited by 27 publications

(16 citation statements)

References 27 publications

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“…Forecasting with hybrid models (i.e., combined forecasts from two or more predictive models) has found wide application [6,11,24,[30][31][32][33][34], since it leads to better forecasting performance. For instance, Equation (6) is applied in a case where forecasts from different models are combined in order to obtain a hybrid forecast.…”

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

“…As a result, leading to better forecasting performance [33,36] Useful for real-time, near-optimal control of water distribution systems (WDS) [6]. Necessary for operational purposes [33,36].…”

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

“…This shows that if ARMAX and FFBP-NN are used to generate a hybrid forecast, a better forecast compared to the combination of ARMA and FFBP-NN will be obtained. Hybrid forecasting is necessary for operational purposes because it is useful for real-time near-optimal control of WDS [11,[33][34][35][36][37][38].…”

Section: Recommendations Of Stwd Forecasting Models and Future Workmentioning

confidence: 99%

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Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction

Anele

Hamam

Abu‐Mahfouz

et al. 2017

Water

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Abstract:The stochastic nature of water consumption patterns during the day and week varies. Therefore, to continually provide water to consumers with appropriate quality, quantity and pressure, water utilities require accurate and appropriate short-term water demand (STWD) forecasts. In view of this, an overview of forecasting methods for STWD prediction is presented. Based on that, a comparative assessment of the performance of alternative forecasting models from the different methods is studied. Times series models (i.e., autoregressive (AR), moving average (MA), autoregressive-moving average (ARMA), and ARMA with exogenous variable (ARMAX)) introduced by Box and Jenkins (1970), feed-forward back-propagation neural network (FFBP-NN), and hybrid model (i.e., combined forecasts from ARMA and FFBP-NN) are compared with each other for a common set of data. Akaike information criterion (AIC), originally proposed by Akaike (1974) is used to estimate the quality of each short-term forecasting model. Furthermore, Nash-Sutcliffe (NS) model efficiency coefficient proposed by Nash-Sutcliffe (1970), root mean square error (RMSE) and mean absolute percentage error (MAPE) are the forecasting statistical terms used to assess the predictive performance of the models. Lastly, as regards the selection of an accurate and appropriate STWD forecasting model, this paper provides recommendations and future work based on the forecasts generated by each of the predictive models considered.

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Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

Section: Hybrid Forecasting Methodsmentioning

confidence: 99%

Section: Recommendations Of Stwd Forecasting Models and Future Workmentioning

confidence: 99%

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Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction

Anele

Hamam

Abu‐Mahfouz

et al. 2017

Water

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“…Researchers in the water field (Zhou et al, 2000) have examined past water demand by analyzing the daily, weekly, seasonal (monthly or yearly) periodicity (Cutore et al, 2008;Gato et al, 2007) and other variables like climatic indexes (Adrian et al, 1994;Arbués et al, 2003) (Chen et al, 2005), pipe network distribution size (Herrera et al, 2010;Mohamed and AlMualla, 2010), price (Wang et al, 2009a(Wang et al, , 2009b, and others.…”

Section: Current Strategies For Modeling Water and Energy Demandsmentioning

confidence: 99%

Mathematical model as a standard procedure to analyze small and large water distribution networks

Prieto

Murado

Bartlett

et al. 2015

Journal of Cleaner Production

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HIGHLIGHTSA model was developed to simulate domestic water flow> The model was applied to analyze selected domestic areas in Sligo, Ireland> Reliable parameters are generated allowing the analysis of water consumption> The results confirmed the capabilities of this approach identifying water trends> The study provides rigorous criteria to compare and predict water usage . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 4 ABSTRACT Currently, more research to implement and monitor cleaner production practices for distribution and sustainable management of natural and alternative water sources to comply with the demands of the different users while preserving water levels are needed. In this paper, a periodic hourlybased model with meaningful parameters has been developed to analyze and forecast water demand as a function of time, thus enabling a better understanding of the consumption pattern and the condition of the pipe network. The model was tested by investigating the daily water consumption from selected categories of users which were isolated from different distribution networks in Sligo, Ireland. The flow data used was obtained in 15-min intervals and averaged in different time periods for analysis. In all cases, the model fittings obtained were highly consistent and all the parameters showed satisfactory confidence intervals (=0.05), thus demonstrating the reliability of this approach. The model provides a quick analysis revealing the regularities of water demand that could benefit water utility managers and researchers: to obtain optimal regulation and pumping schemes; for planning and design purposes; to control unexpected scenarios that can take place during the distribution of water; the performance of water distribution systems; and to locate possible network failures. In addition, the model parameters can be used as standard criteria for water utilities to compare precisely the water demand between different areas, identify complex trends and analyze the pipe network for managing, auditing and monitoring purposes.

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“…On the other hand, some heuristic algorithms, for example, genetic algorithm (GA) [19][20][21] and particle swarm optimization (PSO) algorithm [22,23], have also emerged in water demand-related forecasting. Without the constraint of continuity of functions, GA is characteristic of inner implicit parallelism and better global searching capability.…”

Section: Introduction Wmentioning

confidence: 99%

City Water Demand Forecasting Based on Improved BP Neural Network

Xing¹,

You²,

Zhang³

et al. 2017

JRST

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City water demand forecasting is of great significance in reducing the cost of electricity consumption and municipal planning. Back-propagation (BP) neural network has been widely adopted in water demand forecasting in recent years. But BP performs unsatisfactorily in terms of training time and global searching ability, so in this paper we improve BP by two heuristic algorithms, namely, genetic algorithm (ga) and particle swarm optimization (PSO), respectively. The testing and verification of the three algorithms (BP, ga+BP, PSo+BP) have been conducted on real-life water demand forecasting of Beijing city. The obtained results demonstrate that, in spite of the execution time consumed, both ga+BP and PSo+BP performed with higher accuracy and less errors than BP. The obtained results also illustrate that PSo+BP slightly outperformed ga+BP in terms of forecasting accuracy.

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An eco-environmental water demand based model for optimising water resources using hybrid genetic simulated annealing algorithms. Part I. Model development

Cited by 27 publications

References 27 publications

Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction

Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction

Mathematical model as a standard procedure to analyze small and large water distribution networks

City Water Demand Forecasting Based on Improved BP Neural Network

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