Hybrid regression model for near real-time urban water demand forecasting

[EN] The most important factor in planning and operating water distribution systems is satisfying consumer demand. This means continuously providing users with quality water in adequate volumes at reasonable pressure, thus ensuring reliable water distribution. In recent years, the application of statistical, machine learning, and artificial intelligence methodologies has been fostered for water demand forecasting. However, there is still room for improvement; and new challenges regarding on-line predictive models for water demand have appeared. This work proposes applying support vector regression, as one of the currently better machine learning options for short-term water demand forecasting, to build a base prediction. On this model, a Fourier time series process is built to improve the base prediction. This addition produces a tool able to eliminate many of the errors and much of the bias inherent in a fixed regression structure when responding to new incoming time series data. The final hybrid process is validated using demand data from a water utility in Franca, Brazil. Our model, being a near real-time model for water demand, may be directly exploited in water management decision-making processes. (C) 2016 Elsevier B.V. All rights reserved.This work has been partially supported by CAPES Foundation of Brazil’s Ministry of Education. The data were provided by SABESP, São Paulo state water management company.Brentan, BM.; Luvizotto, E.; Herrera Fernández, AM.; Izquierdo Sebastián, J.; Pérez García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics. 309:532-541. doi:10.1016/j.cam.2016.02.009S53254130

Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems

[EN] Prediction models have become essential for the improvement of decision-making processes in public management and, particularly, for water supply utilities. Accurate estimation often needs to solve multimeasurement, mixed-mode, and space-time problems, typical of many engineering applications. As a result, accurate estimation of real world variables is still one of the major problems in mathematical approximation. Several individual techniques have shown very good estimation abilities. However, none of them are free from drawbacks. This paper faces the challenge of creating accurate water demand predictive models at urban scale by using so-called committee machines, which are ensemble frameworks of single machine learning models. The proposal is able to combine models of varied nature. Specifically, this paper analyzes combinations of such techniques as multilayer perceptrons, support vector machines, extreme learning machines, random forests, adaptive neural fuzzy inference systems, and the group method for data handling. Analyses are checked on two water demand datasets from Franca (Brazil). As an ensemble tool, the combined response of a committee machine outperforms any single constituent model.Ambrosio, JK.; Brentan, BM.; Herrera Fernández, AM.; Luvizotto, E.; Ribeiro, L.; Izquierdo Sebastián, J. (2019). Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems. Mathematical Problems in Engineering. 2019:1-11. https://doi.org/10.1155/2019/97654681112019Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2010). Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems. Engineering Applications of Artificial Intelligence, 23(5), 727-735. doi:10.1016/j.engappai.2010.01.015Donkor, E. A., Mazzuchi, T. A., Soyer, R., & Alan Roberson, J. (2014). Urban Water Demand Forecasting: Review of Methods and Models. Journal of Water Resources Planning and Management, 140(2), 146-159. doi:10.1061/(asce)wr.1943-5452.0000314Adamowski, J. F. (2008). Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks. Journal of Water Resources Planning and Management, 134(2), 119-128. doi:10.1061/(asce)0733-9496(2008)134:2(119)Ghiassi, M., Zimbra, D. K., & Saidane, H. (2008). Urban Water Demand Forecasting with a Dynamic Artificial Neural Network Model. Journal of Water Resources Planning and Management, 134(2), 138-146. doi:10.1061/(asce)0733-9496(2008)134:2(138)Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5(4), 559-583. doi:10.1016/0169-2070(89)90012-5Herrera, M., García-Díaz, J. C., Izquierdo, J., & Pérez-García, R. (2011). Municipal Water Demand Forecasting: Tools for Intervention Time Series. Stochastic Analysis and Applications, 29(6), 998-1007. doi:10.1080/07362994.2011.610161Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Barzegar, R., & Asghari Moghaddam, A. (2016). Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction. Modeling Earth Systems and Environment, 2(1). doi:10.1007/s40808-015-0072-8Nadiri, A. A., Gharekhani, M., Khatibi, R., Sadeghfam, S., & Moghaddam, A. A. (2017). Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM). Science of The Total Environment, 574, 691-706. doi:10.1016/j.scitotenv.2016.09.093Brentan, B. M., Meirelles, G., Herrera, M., Luvizotto, E., & Izquierdo, J. (2017). Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models. Mathematical Problems in Engineering, 2017, 1-10. doi:10.1155/2017/6343625Brentan, B. M., Luvizotto Jr., E., Herrera, M., Izquierdo, J., & Pérez-García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics, 309, 532-541. doi:10.1016/j.cam.2016.02.009Johansson, C., Bergkvist, M., Geysen, D., Somer, O. D., Lavesson, N., & Vanhoudt, D. (2017). Operational Demand Forecasting In District Heating Systems Using Ensembles Of Online Machine Learning Algorithms. Energy Procedia, 116, 208-216. doi:10.1016/j.egypro.2017.05.068Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3), 21-45. doi:10.1109/mcas.2006.1688199Ferreira, R. P., Martiniano, A., Ferreira, A., Ferreira, A., & Sassi, R. J. (2016). Study on Daily Demand Forecasting Orders using Artificial Neural Network. 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Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework

[EN] Epidemiology-based models have shown to have successful adaptations to deal with challenges coming from various areas of Engineering, such as those related to energy use or asset management. This paper deals with urban water demand, and data analysis is based on an Epidemiology tool-set herein developed. This combination represents a novel framework in urban hydraulics. Specifically, various reduction tools for time series analyses based on a symbolic approximate (SAX) coding technique able to deal with simple versions of data sets are presented. Then, a neural-network-based model that uses SAX-based knowledge-generation from various time series is shown to improve forecasting abilities. This knowledge is produced by identifying water distribution district metered areas of high similarity to a given target area and sharing demand patterns with the latter. The proposal has been tested with databases from a Brazilian water utility, providing key knowledge for improving water management and hydraulic operation of the distribution system. This novel analysis framework shows several benefits in terms of accuracy and performance of neural network models for water demand.Navarrete-López, CF.; Herrera Fernández, AM.; Brentan, BM.; Luvizotto Jr., E.; Izquierdo Sebastián, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water. 11(246):1-17. https://doi.org/10.3390/w11020246S11711246Fecarotta, O., Carravetta, A., Morani, M., & Padulano, R. (2018). Optimal Pump Scheduling for Urban Drainage under Variable Flow Conditions. Resources, 7(4), 73. doi:10.3390/resources7040073Creaco, E., & Pezzinga, G. (2018). Comparison of Algorithms for the Optimal Location of Control Valves for Leakage Reduction in WDNs. Water, 10(4), 466. doi:10.3390/w10040466Nguyen, K. A., Stewart, R. A., Zhang, H., Sahin, O., & Siriwardene, N. (2018). Re-engineering traditional urban water management practices with smart metering and informatics. Environmental Modelling & Software, 101, 256-267. doi:10.1016/j.envsoft.2017.12.015Adamowski, J., & Karapataki, C. (2010). Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms. Journal of Hydrologic Engineering, 15(10), 729-743. doi:10.1061/(asce)he.1943-5584.0000245Caiado, J. (2010). Performance of Combined Double Seasonal Univariate Time Series Models for Forecasting Water Demand. Journal of Hydrologic Engineering, 15(3), 215-222. doi:10.1061/(asce)he.1943-5584.0000182Herrera, M., Torgo, L., Izquierdo, J., & Pérez-García, R. (2010). Predictive models for forecasting hourly urban water demand. Journal of Hydrology, 387(1-2), 141-150. doi:10.1016/j.jhydrol.2010.04.005Msiza, I. S., Nelwamondo, F. V., & Marwala, T. (2008). 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Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175. doi:10.1016/s0925-2312(01)00702-0Herrera, M., García-Díaz, J. C., Izquierdo, J., & Pérez-García, R. (2011). Municipal Water Demand Forecasting: Tools for Intervention Time Series. Stochastic Analysis and Applications, 29(6), 998-1007. doi:10.1080/07362994.2011.610161Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664-2675. doi:10.1016/j.asoc.2010.10.015Campisi-Pinto, S., Adamowski, J., & Oron, G. (2012). Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy. Water Resources Management, 26(12), 3539-3558. doi:10.1007/s11269-012-0089-yBrentan, B. M., Luvizotto Jr., E., Herrera, M., Izquierdo, J., & Pérez-García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. 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Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series

open access articleShort-term water demand forecasting plays an important role in smart management and real-time simulation of water distribution systems (WDSs). This paper proposes a hybrid model for the short-term forecasting in the horizon of one day with 15 minute time steps, which improves the forecasting accuracy by adding an error correction module to the initial forecasting model. The initial forecasting model is firstly established based on the least square support vector machine (LSSVM), the errors time series obtained by comparing the observed values and the initial forecasted values is next transformed into chaotic time series, and then the error correction model is established by the LSSVM method to forecast errors at the next time step. The hybrid model is tested on three real-world district metering areas (DMAs) in Beijing, China, with different demand patterns. The results show that, with the help of the error correction module, the hybrid model reduced the mean absolute percentage error (MAPE) of forecasted demand from (5.64%, 4.06%, 5.84%) to (4.84%, 3.15%, 3.47%) for the three DMAs, compared with using LSSVM without error correction. Therefore, the proposed hybrid model provides a better solution for short-term water demand forecasting on the tested cases

Enhanced water demand analysis via symbolic approximation within an epidemiology-based forecasting framework

Epidemiology-based models have shown to have successful adaptations to deal with challenges coming from various areas of Engineering, such as those related to energy use or asset management. This paper deals with urban water demand, and data analysis is based on an Epidemiology tool-set herein developed. This combination represents a novel framework in urban hydraulics. Specifically, various reduction tools for time series analyses based on a symbolic approximate (SAX) coding technique able to deal with simple versions of data sets are presented. Then, a neural-network-based model that uses SAX-based knowledge-generation from various time series is shown to improve forecasting abilities. This knowledge is produced by identifying water distribution district metered areas of high similarity to a given target area and sharing demand patterns with the latter. The proposal has been tested with databases from a Brazilian water utility, providing key knowledge for improving water management and hydraulic operation of the distribution system. This novel analysis framework shows several benefits in terms of accuracy and performance of neural network models for water demand112sem informaçãosem informaçã

A short-term data based water consumption prediction approach

A smart water network consists of a large number of devices that measure a wide range of parameters present in distribution networks in an automatic and continuous way. Among these data, you can find the flow, pressure, or totalizer measurements that, when processed with appropriate algorithms, allow for leakage detection at an early stage. These algorithms are mainly based on water demand forecasting. Different approaches for the prediction of water demand are available in the literature. Although they present successful results at different levels, they have two main drawbacks: the inclusion of several seasonalities is quite cumbersome, and the fitting horizons are not very large. With the aim of solving these problems, we present the application of pattern similarity-based techniques to the water demand forecasting problem. The use of these techniques removes the need to determine the annual seasonality and, at the same time, extends the horizon of prediction to 24 h. The algorithm has been tested in the context of a real project for the detection and location of leaks at an early stage by means of demand forecasting, and good results were obtained, which are also presented in this paper.publishersversionpublishe

Near real time pump optimization and pressure management

[EN] Management of existing systems can be interpreted as sets of decisions to make regarding pumps and valves to create hydraulic conditions able to satisfy the demand without operational problems such as pressures lower or higher than the normative pressure values. However, among the large number of combinations, some of them manage to reduce energy consumption, by finding the best operating point for pumps, and also water losses, by finding the best operating point for pressure reducing valves (PRV). Several works may be found in the literature using recent and advanced optimization techniques to define pump and valve operation. However, the processing time to define operational rules is a limiting factor for real time decision-making. Taking into account the need to improve the models in terms of optimal rules to apply in near real-time operations, this work presents a hybrid model (simulator + optimizer) to find pump speeds and PRV set points, aiming at combining energy savings with pressure control while reducing water losses. PSO is applied as the main optimization algorithm, which can also work in cooperation with other bio-inspired concepts to deploy an effective and fast search algorithm. The results allow comparisons with other techniques and show the ability of PSO to find an optimal point of operationBrentan, BM.; Luvizotto, EJ.; Montalvo, I.; Izquierdo Sebastián, J.; Pérez García, R. (2017). Near real time pump optimization and pressure management. Procedia Engineering. 186:666-675. doi:10.1016/j.proeng.2017.06.248S66667518