Gerenciamento ótimo das pressões em redes de abastecimento de água através da criação de distritos de medição com base na aprendizagem de máquinas

Integrated management of water supply systems with efficient use of natural resources requires optimization of operational performances. Dividing the water supply networks into small units, so-called district metered areas (DMAs), is a strategy that allows the development of specific operational rules, responsible for improving the network performance. In this context, clustering methods congregate neighboring nodes in groups according to similar features, such as elevation or distance to the water source. Taking into account hydraulic, operational and mathematical criteria to determine the configuration of DMAs, this work presents the k-means model and a hybrid model, that combines a self-organizing map (SOM) with the k-means algorithm, as clustering methods, comparing four mathematical criteria to determine the number of DMAs, namely Silhouette, GAP, Calinski-Harabasz and Davies Bouldin. The influence of three clustering topological criteria is evaluated: the water demand, node elevation and pipe length, in order to determine the optimal number of clusters. Furthermore, to identify the best DMA configuration, the particle swarm optimization (PSO) method was applied to determine the number, cost, pressure setting of Pressure Reducing Valves and location of DMA entrances.24A gestão integrada dos sistemas de abastecimento de água com o uso eficiente dos recursos requer a otimização das operações. O agrupamento das redes de abastecimento de água em pequenas unidades, chamadas de distritos de medição (DMAs), é uma estratégia que permite o desenvolvimento de regras operacionais específicas, responsáveis por melhorar o desempenho da rede. Neste contexto, os métodos de classificação agrupam os nós vizinhos de acordo com características semelhantes, como elevação ou distância à fonte de água. Utilizando os critérios topológicos, operacionais e matemáticos para determinar a configuração dos DMAs, o trabalho apresenta um modelo k-means e um modelo híbrido, que combina um mapa auto-organizado (SOM) com o algoritmo k-means, como métodos de agrupamento. Comparou-se quatro critérios matemáticos, Silhouette, GAP, CalinskiHarabasz e Davies-Bouldin e analisou-se a influência de três critérios topológicos variáveis, a demanda de água, a elevação dos nós e o comprimento do tubo, para determinar o número ótimo de agrupamentos. Ademais, com o intuito de identificar a melhor configuração de DMAs, o método de otimização de enxame de partículas (PSO) foi aplicado para determinar o número, o custo, as pressões e a localização das entradas do DMA

Hybrid SOM+k-Means Clustering to Improve Planning, Operation and Management in Water Distribution Systems

[EN] With the advance of new technologies and emergence of the concept of the smart city, there has been a dramatic increase in available information. Water distribution systems (WDSs) in which databases can be updated every few minutes are no exception. Suitable techniques to evaluate available information and produce optimized responses are necessary for planning, operation, and management. This can help identify critical characteristics, such as leakage patterns, pipes to be replaced, and other features. This paper presents a clustering method based on self-organizing maps coupled with k-means algorithms to achieve groups that can be easily labeled and used for WDS decision-making. Three case-studies are presented, namely a classification of Brazilian cities in terms of their water utilities; district metered area creation to improve pressure control; and transient pressure signal analysis to identify burst pipes. In the three cases, this hybrid technique produces excellent results. © 2018 Elsevier Ltd. All rights reserved.This work is partially supported by Capes and CNPq, Brazilian research agencies. The use of English was revised by John Rawlins.Brentan, BM.; Meirelles, G.; Luvizotto, E.; Izquierdo Sebastián, J. (2018). Hybrid SOM+k-Means Clustering to Improve Planning, Operation and Management in Water Distribution Systems. Environmental Modelling & Software. 106:77-88. https://doi.org/10.1016/j.envsoft.2018.02.013S778810

Joint operation of pressure reducing valves and pumps for improving the efficiency of water distribution systems

[EN] New environmental paradigms imposed by climate change and urbanization processes are leading cities to rethink urban management services. Propelled by technological development and the internet of things, an increasingly smart management of cities has favored the emergence of a new research field, namely, the smart city. Within this new way of considering cities, smart water systems are emerging for the planning, operation, and management of water distribution networks (WDNs) with maximum efficiency derived from the application of data analysis and other information technology tools. Considering the possibility of improving WDN operation using available demand data, this work proposes a hybrid and near-real-time optimization algorithm to jointly manage pumps working with variable speed drives and pressure-reducing valves for maximum operational efficiency. A near-real-time demand forecasting model is coupled with an optimization algorithm that updates in real time the water demand of the hydraulic model and can be used to define optimal operations. The D-town WDN is used to validate the proposal. The number of control devices in this WDN makes real time control especially complex. Warm solutions are proposed to cope with this feature as they reduce the computational effort needed if suitably tuned. In addition to energy savings of around 50%, the methodology proposed in this paper enables an efficient system pressure management, leading to significant leakage reduction.Brentan, BM.; Meirelles, G.; Luvizotto, E.; Izquierdo Sebastián, J. (2018). Joint operation of pressure reducing valves and pumps for improving the efficiency of water distribution systems. Journal of Water Resources Planning and Management. 144(9):04018055-1-04018055-12. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000974S04018055-104018055-12144

Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models

Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs) management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA) and machine learning powerful algorithms such as Self-Organizing Maps (SOMs) and Random Forest (RF). We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs) from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model

District metered area design through multicriteria and multiobjective optimization

The design of district metered areas (DMA) in potable water supply systems is of paramount importance for water utilities to properly manage their systems. Concomitant to their main objective, namely, to deliver quality water to consumers, the benefits include leakage reduction and prompt reaction in cases of natural or malicious contamination events. Given the structure of a water distribution network (WDN), graph theory is the basis for DMA design, and clustering algorithms can be applied to perform the partitioning. However, such sectorization entails a number of network modifications (installing cut-off valves and metering and control devices) involving costs and operation changes, which have to be carefully studied and optimized. Given the complexity of WDNs, optimization is usually performed using metaheuristic algorithms. In turn, optimization may be single or multiple-objective. In this last case, a large number of solutions, frequently integrating the Pareto front, may be produced. The decision maker has eventually to choose one among them, what may be tough task. Multicriteria decision methods may be applied to support this last step of the decision-making process. In this paper, DMA design is addressed by (i) proposing a modified k-means algorithm for partitioning, (ii) using a multiobjective particle swarm optimization to suitably place partitioning devices, (iii) using fuzzy analytic hierarchy process (FAHP) to weight the four objective functions considered, and (iv) using technique for order of preference by similarity to ideal solution (TOPSIS) to rank the Pareto solutions to support the decision. This joint approach is applied in a case of a well-known WDN of the literature, and the results are discussed

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. IEEE Latin America Transactions, 14(3), 1519-1525. doi:10.1109/tla.2016.7459644Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. doi:10.1007/bf00994018Schölkop, B. (2003). An Introduction to Support Vector Machines. Recent Advances and Trends in Nonparametric Statistics, 3-17. doi:10.1016/b978-044451378-6/50001-6Huang, G.-B., Wang, D. H., & Lan, Y. (2011). Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics, 2(2), 107-122. doi:10.1007/s13042-011-0019-yIvakhnenko, A. G. (1970). Heuristic self-organization in problems of engineering cybernetics. Automatica, 6(2), 207-219. doi:10.1016/0005-1098(70)90092-

Trunk Network Rehabilitation for Resilience Improvement and Energy Recovery in Water Distribution Networks

[EN] Water distribution networks (WDNs) are designed to meet water demand with minimum implementation costs. However, this approach leads to poor long-term results, since system resilience is also minimal, and this requires the rehabilitation of the network if the network is expanded or the demand increases. In addition, in emergency situations, such as pipe bursts, large areas will suffer water shortage. However, the use of resilience as a criterion for WDN design is a difficult task, since its economic value is subjective. Thus, in this paper, it is proposed that trunk networks (TNs) are rehabilitated when considering the generation of electrical energy using pumps as turbines (PATs) to compensate for an increase of resilience derived from increasing pipe diameters. During normal operation, these micro-hydros will control pressure and produce electricity. When an emergency occurs, a by-pass can be used to increase network pressure. The results that were obtained for two hypothetical networks show that a small increase in TN pipe diameters is sufficient to significantly improve the resilience of the WDN. In addition, the value of the energy produced surpasses the investment that is made during rehabilitation.The authors wish to thank the project REDAWN (Reducing Energy Dependency in Atlantic Area Water Networks) EAPA_198/2016 from INTERREG ATLANTIC AREA PROGRAMME 2014-2020.Meirelles, G.; Brentan, BM.; Izquierdo Sebastián, J.; Ramos, HM.; Luvizotto, E. (2018). Trunk Network Rehabilitation for Resilience Improvement and Energy Recovery in Water Distribution Networks. Water. 10(6):1-14. https://doi.org/10.3390/w10060693S114106Zong Woo Geem, Joong Hoon Kim, & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION, 76(2), 60-68. doi:10.1177/003754970107600201Maier, H. R., Simpson, A. R., Zecchin, A. C., Foong, W. K., Phang, K. Y., Seah, H. Y., & Tan, C. L. (2003). Ant Colony Optimization for Design of Water Distribution Systems. Journal of Water Resources Planning and Management, 129(3), 200-209. doi:10.1061/(asce)0733-9496(2003)129:3(200)Suribabu, C. R., & Neelakantan, T. R. (2006). Design of water distribution networks using particle swarm optimization. Urban Water Journal, 3(2), 111-120. doi:10.1080/15730620600855928Baños, R., Reca, J., Martínez, J., Gil, C., & Márquez, A. L. (2011). Resilience Indexes for Water Distribution Network Design: A Performance Analysis Under Demand Uncertainty. Water Resources Management, 25(10), 2351-2366. doi:10.1007/s11269-011-9812-3Shokoohi, M., Tabesh, M., Nazif, S., & Dini, M. (2016). Water Quality Based Multi-objective Optimal Design of Water Distribution Systems. Water Resources Management, 31(1), 93-108. doi:10.1007/s11269-016-1512-6Marques, J., Cunha, M., & Savić, D. (2015). Using Real Options in the Optimal Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 141(2), 04014052. doi:10.1061/(asce)wr.1943-5452.0000448Schwartz, R., Housh, M., & Ostfeld, A. (2016). Least-Cost Robust Design Optimization of Water Distribution Systems under Multiple Loading. Journal of Water Resources Planning and Management, 142(9), 04016031. doi:10.1061/(asce)wr.1943-5452.0000670Giustolisi, O., Laucelli, D., & Colombo, A. F. (2009). Deterministic versus Stochastic Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 135(2), 117-127. doi:10.1061/(asce)0733-9496(2009)135:2(117)Lansey, K. E., Duan, N., Mays, L. W., & Tung, Y. (1989). Water Distribution System Design Under Uncertainties. Journal of Water Resources Planning and Management, 115(5), 630-645. doi:10.1061/(asce)0733-9496(1989)115:5(630)Zheng, F., Simpson, A., & Zecchin, A. (2015). Improving the efficiency of multi-objective evolutionary algorithms through decomposition: An application to water distribution network design. Environmental Modelling & Software, 69, 240-252. doi:10.1016/j.envsoft.2014.08.022Geem, Z. (2015). Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search. Water, 7(12), 3613-3625. doi:10.3390/w7073613Prasad, T. D., & Park, N.-S. (2004). Multiobjective Genetic Algorithms for Design of Water Distribution Networks. Journal of Water Resources Planning and Management, 130(1), 73-82. doi:10.1061/(asce)0733-9496(2004)130:1(73)Pérez-Sánchez, M., Sánchez-Romero, F., Ramos, H., & López-Jiménez, P. (2017). Energy Recovery in Existing Water Networks: Towards Greater Sustainability. Water, 9(2), 97. doi:10.3390/w9020097De Marchis, M., & Freni, G. (2015). Pump as turbine implementation in a dynamic numerical model: cost analysis for energy recovery in water distribution network. Journal of Hydroinformatics, 17(3), 347-360. doi:10.2166/hydro.2015.018Carravetta, A., del Giudice, G., Fecarotta, O., & Ramos, H. (2013). PAT Design Strategy for Energy Recovery in Water Distribution Networks by Electrical Regulation. Energies, 6(1), 411-424. doi:10.3390/en6010411Lima, G. M., Luvizotto, E., Brentan, B. M., & Ramos, H. M. (2018). Leakage Control and Energy Recovery Using Variable Speed Pumps as Turbines. Journal of Water Resources Planning and Management, 144(1), 04017077. doi:10.1061/(asce)wr.1943-5452.0000852Carravetta, A., Del Giudice, G., Fecarotta, O., & Ramos, H. M. (2012). Energy Production in Water Distribution Networks: A PAT Design Strategy. Water Resources Management, 26(13), 3947-3959. doi:10.1007/s11269-012-0114-1Lydon, T., Coughlan, P., & McNabola, A. (2017). Pump-As-Turbine: Characterization as an Energy Recovery Device for the Water Distribution Network. Journal of Hydraulic Engineering, 143(8), 04017020. doi:10.1061/(asce)hy.1943-7900.0001316Afshar, A., Jemaa, F. B., & Mariño, M. A. (1990). Optimization of Hydropower Plant Integration in Water Supply System. Journal of Water Resources Planning and Management, 116(5), 665-675. doi:10.1061/(asce)0733-9496(1990)116:5(665)Meirelles Lima, G., Brentan, B. M., & Luvizotto, E. (2018). Optimal design of water supply networks using an energy recovery approach. Renewable Energy, 117, 404-413. doi:10.1016/j.renene.2017.10.080Campbell, E., Izquierdo, J., Montalvo, I., Ilaya-Ayza, A., Pérez-García, R., & Tavera, M. (2015). A flexible methodology to sectorize water supply networks based on social network theory concepts and multi-objective optimization. Journal of Hydroinformatics, 18(1), 62-76. doi:10.2166/hydro.2015.146Di Nardo, A., Di Natale, M., Giudicianni, C., Greco, R., & Santonastaso, G. F. (2017). Complex network and fractal theory for the assessment of water distribution network resilience to pipe failures. Water Supply, 18(3), 767-777. doi:10.2166/ws.2017.124Bragalli, C., D’Ambrosio, C., Lee, J., Lodi, A., & Toth, P. (2011). On the optimal design of water distribution networks: a practical MINLP approach. Optimization and Engineering, 13(2), 219-246. doi:10.1007/s11081-011-9141-7Reca, J., & Martínez, J. (2006). Genetic algorithms for the design of looped irrigation water distribution networks. Water Resources Research, 42(5). doi:10.1029/2005wr004383Di Nardo, A., Di Natale, M., Santonastaso, G. F., Tzatchkov, V. G., & Alcocer-Yamanaka, V. H. (2014). Water Network Sectorization Based on Graph Theory and Energy Performance Indices. Journal of Water Resources Planning and Management, 140(5), 620-629. doi:10.1061/(asce)wr.1943-5452.0000364Hajebi, S., Temate, S., Barrett, S., Clarke, A., & Clarke, S. (2014). Water Distribution Network Sectorisation Using Structural Graph Partitioning and Multi-objective Optimization. Procedia Engineering, 89, 1144-1151. doi:10.1016/j.proeng.2014.11.238Todini, E. (2000). Looped water distribution networks design using a resilience index based heuristic approach. Urban Water, 2(2), 115-122. doi:10.1016/s1462-0758(00)00049-2Brentan, B. M., Campbell, E., Meirelles, G. L., Luvizotto, E., & Izquierdo, J. (2017). Social Network Community Detection for DMA Creation: Criteria Analysis through Multilevel Optimization. Mathematical Problems in Engineering, 2017, 1-12. doi:10.1155/2017/9053238Lima, G. M., Luvizotto, E., & Brentan, B. M. (2017). Selection and location of Pumps as Turbines substituting pressure reducing valves. Renewable Energy, 109, 392-405. doi:10.1016/j.renene.2017.03.056Letting, L., Hamam, Y., & Abu-Mahfouz, A. (2017). Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization. Water, 9(8), 593. doi:10.3390/w908059

Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location

[EN] A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important-in many cases the greatest-fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization.Manzi, D.; Brentan, BM.; Meirelles, G.; Izquierdo Sebastián, J.; Luvizotto Jr., E. (2019). Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location. Water. 11(11):1-13. https://doi.org/10.3390/w11112279S1131111Creaco, E., & Walski, T. (2017). Economic Analysis of Pressure Control for Leakage and Pipe Burst Reduction. Journal of Water Resources Planning and Management, 143(12), 04017074. doi:10.1061/(asce)wr.1943-5452.0000846Campisano, A., Creaco, E., & Modica, C. (2010). RTC of Valves for Leakage Reduction in Water Supply Networks. Journal of Water Resources Planning and Management, 136(1), 138-141. doi:10.1061/(asce)0733-9496(2010)136:1(138)Campisano, A., Modica, C., Reitano, S., Ugarelli, R., & Bagherian, S. (2016). Field-Oriented Methodology for Real-Time Pressure Control to Reduce Leakage in Water Distribution Networks. Journal of Water Resources Planning and Management, 142(12), 04016057. doi:10.1061/(asce)wr.1943-5452.0000697Vítkovský, J. P., Simpson, A. R., & Lambert, M. F. (2000). Leak Detection and Calibration Using Transients and Genetic Algorithms. Journal of Water Resources Planning and Management, 126(4), 262-265. doi:10.1061/(asce)0733-9496(2000)126:4(262)Pérez, R., Puig, V., Pascual, J., Quevedo, J., Landeros, E., & Peralta, A. (2011). Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Engineering Practice, 19(10), 1157-1167. doi:10.1016/j.conengprac.2011.06.004Jung, D., & Kim, J. (2017). Robust Meter Network for Water Distribution Pipe Burst Detection. Water, 9(11), 820. doi:10.3390/w9110820Colombo, A. F., Lee, P., & Karney, B. W. (2009). A selective literature review of transient-based leak detection methods. Journal of Hydro-environment Research, 2(4), 212-227. doi:10.1016/j.jher.2009.02.003Choi, D., Kim, S.-W., Choi, M.-A., & Geem, Z. (2016). Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System. Water, 8(4), 142. doi:10.3390/w8040142Christodoulou, S. E., Kourti, E., & Agathokleous, A. (2016). Waterloss Detection in Water Distribution Networks using Wavelet Change-Point Detection. Water Resources Management, 31(3), 979-994. doi:10.1007/s11269-016-1558-5Guo, X., Yang, K., & Guo, Y. (2012). Leak detection in pipelines by exclusively frequency domain method. Science China Technological Sciences, 55(3), 743-752. doi:10.1007/s11431-011-4707-3Holloway, M. B., & Hanif Chaudhry, M. (1985). Stability and accuracy of waterhammer analysis. Advances in Water Resources, 8(3), 121-128. doi:10.1016/0309-1708(85)90052-1Sanz, G., Pérez, R., Kapelan, Z., & Savic, D. (2016). Leak Detection and Localization through Demand Components Calibration. Journal of Water Resources Planning and Management, 142(2), 04015057. doi:10.1061/(asce)wr.1943-5452.0000592Zhang, Q., Wu, Z. Y., Zhao, M., Qi, J., Huang, Y., & Zhao, H. (2016). Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines. Journal of Water Resources Planning and Management, 142(11), 04016042. doi:10.1061/(asce)wr.1943-5452.0000661Mounce, S. R., & Machell, J. (2006). Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal, 3(1), 21-31. doi:10.1080/15730620600578538Covas, D., Ramos, H., & de Almeida, A. B. (2005). Standing Wave Difference Method for Leak Detection in Pipeline Systems. Journal of Hydraulic Engineering, 131(12), 1106-1116. doi:10.1061/(asce)0733-9429(2005)131:12(1106)Liggett, J. A., & Chen, L. (1994). Inverse Transient Analysis in Pipe Networks. Journal of Hydraulic Engineering, 120(8), 934-955. doi:10.1061/(asce)0733-9429(1994)120:8(934)Caputo, A. C., & Pelagagge, P. M. (2002). An inverse approach for piping networks monitoring. Journal of Loss Prevention in the Process Industries, 15(6), 497-505. doi:10.1016/s0950-4230(02)00036-0Van Zyl, J. E. (2014). Theoretical Modeling of Pressure and Leakage in Water Distribution Systems. Procedia Engineering, 89, 273-277. doi:10.1016/j.proeng.2014.11.187Izquierdo, J., & Iglesias, P. . (2004). Mathematical modelling of hydraulic transients in complex systems. Mathematical and Computer Modelling, 39(4-5), 529-540. doi:10.1016/s0895-7177(04)90524-9Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zNavarrete-López, C., Herrera, M., Brentan, B., Luvizotto, E., & Izquierdo, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water, 11(2), 246. doi:10.3390/w11020246Meirelles, G., Manzi, D., Brentan, B., Goulart, T., & Luvizotto, E. (2017). Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks. Water Resources Management, 31(13), 4339-4351. doi:10.1007/s11269-017-1750-2Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4), 28-40. doi:10.1016/j.jhydrol.2011.06.013Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Hybrid SOM+ k -Means clustering to improve planning, operation and management in water distribution systems. Environmental Modelling & Software, 106, 77-88. doi:10.1016/j.envsoft.2018.02.013Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3(1), 1-27. doi:10.1080/0361092740882710

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). Water Demand Prediction using Artificial Neural Networks and Support Vector Regression. Journal of Computers, 3(11). doi:10.4304/jcp.3.11.1-8Tiwari, M., Adamowski, J., & Adamowski, K. (2016). Water demand forecasting using extreme learning machines. Journal of Water and Land Development, 28(1), 37-52. doi:10.1515/jwld-2016-0004Vijayalaksmi, D. P., & Babu, K. S. J. (2015). Water Supply System Demand Forecasting Using Adaptive Neuro-fuzzy Inference System. Aquatic Procedia, 4, 950-956. doi:10.1016/j.aqpro.2015.02.119Zhou, L., Xia, J., Yu, L., Wang, Y., Shi, Y., Cai, S., & Nie, S. (2016). Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans. International Journal of Environmental Research and Public Health, 13(4), 355. doi:10.3390/ijerph13040355Cadenas, E., Rivera, W., Campos-Amezcua, R., & Heard, C. (2016). Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model. Energies, 9(2), 109. doi:10.3390/en9020109Zhang, G. P. (2003). 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. Journal of Computational and Applied Mathematics, 309, 532-541. doi:10.1016/j.cam.2016.02.009Di Nardo, A., Di Natale, M., Musmarra, D., Santonastaso, G. F., Tzatchkov, V., & Alcocer-Yamanaka, V. H. (2014). Dual-use value of network partitioning for water system management and protection from malicious contamination. Journal of Hydroinformatics, 17(3), 361-376. doi:10.2166/hydro.2014.014Scarpa, F., Lobba, A., & Becciu, G. (2016). Elementary DMA Design of Looped Water Distribution Networks with Multiple Sources. Journal of Water Resources Planning and Management, 142(6), 04016011. doi:10.1061/(asce)wr.1943-5452.0000639Panagopoulos, G. P., Bathrellos, G. D., Skilodimou, H. D., & Martsouka, F. A. (2012). Mapping Urban Water Demands Using Multi-Criteria Analysis and GIS. Water Resources Management, 26(5), 1347-1363. doi:10.1007/s11269-011-9962-3Buchberger, S. G., & Nadimpalli, G. (2004). Leak Estimation in Water Distribution Systems by Statistical Analysis of Flow Readings. Journal of Water Resources Planning and Management, 130(4), 321-329. doi:10.1061/(asce)0733-9496(2004)130:4(321)Candelieri, A. (2017). Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection. Water, 9(3), 224. doi:10.3390/w9030224Padulano, R., & Del Giudice, G. (2018). Pattern Detection and Scaling Laws of Daily Water Demand by SOM: an Application to the WDN of Naples, Italy. Water Resources Management, 33(2), 739-755. doi:10.1007/s11269-018-2140-0Bloetscher, F. (2012). Protecting People, Infrastructure, Economies, and Ecosystem Assets: Water Management in the Face of Climate Change. Water, 4(2), 367-388. doi:10.3390/w4020367Bach, P. M., Rauch, W., Mikkelsen, P. S., McCarthy, D. T., & Deletic, A. (2014). A critical review of integrated urban water modelling – Urban drainage and beyond. Environmental Modelling & Software, 54, 88-107. doi:10.1016/j.envsoft.2013.12.018Goltsev, A. V., Dorogovtsev, S. N., Oliveira, J. G., & Mendes, J. F. F. (2012). Localization and Spreading of Diseases in Complex Networks. Physical Review Letters, 109(12). doi:10.1103/physrevlett.109.128702Danila, B., Yu, Y., Marsh, J. A., & Bassler, K. E. (2006). Optimal transport on complex networks. Physical Review E, 74(4). doi:10.1103/physreve.74.046106Herrera, M., Izquierdo, J., Pérez-García, R., & Montalvo, I. (2012). Multi-agent adaptive boosting on semi-supervised water supply clusters. Advances in Engineering Software, 50, 131-136. doi:10.1016/j.advengsoft.2012.02.005Maslov, S., Sneppen, K., & Zaliznyak, A. (2004). Detection of topological patterns in complex networks: correlation profile of the internet. Physica A: Statistical Mechanics and its Applications, 333, 529-540. doi:10.1016/j.physa.2003.06.002Lloyd, A. L., & Valeika, S. (2007). Network models in epidemiology: an overview. World Scientific Lecture Notes in Complex Systems, 189-214. doi:10.1142/9789812771582_0008Hamilton, I., Summerfield, A., Oreszczyn, T., & Ruyssevelt, P. (2017). Using epidemiological methods in energy and buildings research to achieve carbon emission targets. Energy and Buildings, 154, 188-197. doi:10.1016/j.enbuild.2017.08.079Bardet, J.-P., & Little, R. (2014). Epidemiology of urban water distribution systems. Water Resources Research, 50(8), 6447-6465. doi:10.1002/2013wr015017De Domenico, M., Granell, C., Porter, M. A., & Arenas, A. (2016). The physics of spreading processes in multilayer networks. Nature Physics, 12(10), 901-906. doi:10.1038/nphys3865Hamilton, I. G., Summerfield, A. J., Lowe, R., Ruyssevelt, P., Elwell, C. A., & Oreszczyn, T. (2013). Energy epidemiology: a new approach to end-use energy demand research. Building Research & Information, 41(4), 482-497. doi:10.1080/09613218.2013.798142Herrera, M., Ferreira, A. A., Coley, D. A., & de Aquino, R. R. B. (2016). SAX-quantile based multiresolution approach for finding heatwave events in summer temperature time series. AI Communications, 29(6), 725-732. doi:10.3233/aic-160716Padulano, R., & Del Giudice, G. (2018). A Mixed Strategy Based on Self-Organizing Map for Water Demand Pattern Profiling of Large-Size Smart Water Grid Data. Water Resources Management, 32(11), 3671-3685. doi:10.1007/s11269-018-2012-7Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zAghabozorgi, S., & Wah, T. Y. (2014). Clustering of large time series datasets. Intelligent Data Analysis, 18(5), 793-817. doi:10.3233/ida-140669Yuan, J., Wang, Z., Han, M., & Sun, Y. (2015). A lazy associative classifier for time series. Intelligent Data Analysis, 19(5), 983-1002. doi:10.3233/ida-150754Rasheed, F., Alshalalfa, M., & Alhajj, R. (2011). Efficient Periodicity Mining in Time Series Databases Using Suffix Trees. IEEE Transactions on Knowledge and Data Engineering, 23(1), 79-94. doi:10.1109/tkde.2010.76Schmieder, R., & Edwards, R. (2011). Fast Identification and Removal of Sequence Contamination from Genomic and Metagenomic Datasets. PLoS ONE, 6(3), e17288. doi:10.1371/journal.pone.0017288Valimaki, N., Gerlach, W., Dixit, K., & Makinen, V. (2007). Compressed suffix tree a basis for genome-scale sequence analysis. Bioinformatics, 23(5), 629-630. doi:10.1093/bioinformatics/btl681Ezkurdia, I., Juan, D., Rodriguez, J. M., Frankish, A., Diekhans, M., Harrow, J., … Tress, M. L. (2014). Multiple evidence strands suggest that there may be as few as 19 000 human protein-coding genes. Human Molecular Genetics, 23(22), 5866-5878. doi:10.1093/hmg/ddu309Bermudez-Santana, C. I. (2016). APLICACIONES DE LA BIOINFORMÁTICA EN LA MEDICINA: EL GENOMA HUMANO. ¿CÓMO PODEMOS VER TANTO DETALLE? Acta Biológica Colombiana, 21(1Supl), 249-258. doi:10.15446/abc.v21n1supl.51233Cai, L., Li, X., Ghosh, M., & Guo, B. (2009). Stability analysis of an HIV/AIDS epidemic model with treatment. Journal of Computational and Applied Mathematics, 229(1), 313-323. doi:10.1016/j.cam.2008.10.067Jackson, M., & Chen-Charpentier, B. M. (2017). Modeling plant virus propagation with delays. Journal of Computational and Applied Mathematics, 309, 611-621. doi:10.1016/j.cam.2016.04.024Brentan, 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:10.1155/2017/6343625Bhaskaran, K., Gasparrini, A., Hajat, S., Smeeth, L., & Armstrong, B. (2013). Time series regression studies in environmental epidemiology. International Journal of Epidemiology, 42(4), 1187-1195. doi:10.1093/ije/dyt092HELFENSTEIN, U. (1991). The Use of Transfer Function Models, Intervention Analysis and Related Time Series Methods in Epidemiology. International Journal of Epidemiology, 20(3), 808-815. doi:10.1093/ije/20.3.808Herrera, M., Abraham, E., & Stoianov, I. (2016). A Graph-Theoretic Framework for Assessing the Resilience of Sectorised Water Distribution Networks. Water Resources Management, 30(5), 1685-1699. doi:10.1007/s11269-016-1245-6Jung, D., Choi, Y., & Kim, J. (2016). Optimal Node Grouping for Water Distribution System Demand Estimation. Water, 8(4), 160. doi:10.3390/w8040160Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., & Keogh, E. (2012). Experimental comparison of representation methods and distance measures for time series data. Data Mining and Knowledge Discovery, 26(2), 275-309. doi:10.1007/s10618-012-0250-5Cassisi, C., Prestifilippo, M., Cannata, A., Montalto, P., Patanè, D., & Privitera, E. (2016). Probabilistic Reasoning Over Seismic Time Series: Volcano Monitoring by Hidden Markov Models at Mt. Etna. Pure and Applied Geophysics, 173(7), 2365-2386. doi:10.1007/s00024-016-1284-1McCreight, E. M. (1976). A Space-Economical Suffix Tree Construction Algorithm. Journal of the ACM, 23(2), 262-272. doi:10.1145/321941.321946Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T. (2015). Time-series clustering – A decade review. Information Systems, 53, 16-38. doi:10.1016/j.is.2015.04.007Warren Liao, T. (2005). Clustering of time series data—a survey. Pattern Recognition, 38(11), 1857-1874. doi:10.1016/j.patcog.2005.01.02

Assessing downscaling techniques for frequency analysis, total precipitation and rainy day estimation in CMIP6 simulations over hydrological years

General circulation models generate climate simulations on grids with resolutions ranging from 50 to 600 km. The resulting coarse spatial resolution of the model outcomes requires post-processing routines to ensure reliable climate information for practical studies, prompting the widespread application of downscaling techniques. However, assessing the effectiveness of multiple downscaling techniques is essential, as their accuracy varies depending on the objectives of the analysis and the characteristics of the case study. In this context, this study aims to evaluate the performance of downscaling the daily precipitation series in the Metropolitan Region of Belo Horizonte (MRBH), Brazil, with the final scope of performing frequency analyses and estimating total precipitation and the number of rainy days per hydrological year at both annual and multiannual levels. To develop this study, 78 climate model simulations with a horizontal resolution of 100 km, which participated in the SSP1-2.6 and/or SSP5-8.5 scenarios of CMIP6, are employed. The results highlight that adjusting the simulations from the general circulation models by the delta method, quantile mapping and regression trees produces accurate results for estimating the total precipitation and number of rainy days. Finally, it is noted that employing downscaled precipitation series through quantile mapping and regression trees also yields promising results in terms of the frequency analyses.</p