Multi-criteria analysis applied to multi-objective optimal pump scheduling in water systems

This work presents a multi-criteria-based approach to automatically select specific non-dominated solutions from a Pareto front previously obtained using multi-objective optimization to find optimal solutions for pump control in a water supply system. Optimal operation of pumps in these utilities is paramount to enable water companies to achieve energy efficiency in their systems. The Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is used to rank the Pareto solutions found by the Non-Dominated Sorting Genetic Algorithm (NSGA-II) employed to solve the multi-objective problem. Various scenarios are evaluated under leakage uncertainty conditions, resulting in fuzzy solutions for the Pareto front. This paper shows the suitability of the approach for quasi real-world problems. In our case-study, the obtained solutions for scenarios including leakage represent the best trade-off among the optimal solutions, under some considered criteria, namely, operational cost, operational lack of service, pressure uniformity and network resilience. Potential future developments could include the use of clustering alternatives to evaluate the goodness of each solution under the considered evaluation criteria

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

Social Network Community Detection for DMA Creation: Criteria Analysis through Multilevel Optimization

[EN] Management of large water distribution systems can be improved by dividing their networks into so-called district metered areas (DMAs). However, such divisions must be based on appropriated technical criteria. Considering the importance of deeply understanding the relationship between DMA creation and these criteria, this work proposes a performance analysis of DMA generation that takes into account such indicators as resilience index, demand similarity, pressure uniformity, water age (and thus water quality), solution implantation costs, and electrical consumption. To cope with the complexity of the problem, suitable mathematical techniques are proposed in this paper. We use a social community detection technique to define the sectors, and then a multilevel particle swarm optimization approach is applied to find the optimal placement and operating point of the necessary devices. The results obtained by implementing themethodology in a real water supply network show its validity and the meaningful influence on the final result of, especially, elevation and pipe length.Brentan, BM.; Campbell-Gonzalez, E.; Meirelles, GL.; Luvizotto, EJ.; Izquierdo Sebastián, J. (2017). Social Network Community Detection for DMA Creation: Criteria Analysis through Multilevel Optimization. Mathematical Problems in Engineering. (9053238):1-12. doi:10.1155/2017/9053238S112905323

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

Pattern recognition and clustering of transient pressure signals for burst location

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 characterization111

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

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

Optimal placement of pressure sensors using fuzzy DEMATEL-based sensor influence

[EN] Nowadays, optimal sensor placement (OSP) for leakage detection in water distribution networks is a lively field of research, and a challenge for water utilities in terms of network control, management, and maintenance. How many sensors to install and where to install them are crucial decisions to make for those utilities to reach a trade-off between efficiency and economy. In this paper, we address the where-to-install-them part of the OSP through the following elements: nodes' sensitivity to leakage, uncertainty of information, and redundancy through conditional entropy maximisation. We evaluate relationships among candidate sensors in a network to get a picture of the mutual influence among the nodes. This analysis is performed within a multi-criteria decision-making approach: specifically, a herein proposed variant of DEMATEL, which uses fuzzy logic and builds comparison matrices derived from information obtained through leakage simulations of the network. We apply the proposal first to a toy example to show how the approach works, and then to a real-world case study.This research has been partially supported by the CNPq grant with number 156213/2018-4.Frances-Chust, J.; Brentan, BM.; Carpitella, S.; Izquierdo Sebastián, J.; Montalvo, I. (2020). Optimal placement of pressure sensors using fuzzy DEMATEL-based sensor influence. Water. 12(2):1-18. https://doi.org/10.3390/w12020493S118122Li, J., Wang, C., Qian, Z., & Lu, C. (2019). Optimal sensor placement for leak localization in water distribution networks based on a novel semi-supervised strategy. Journal of Process Control, 82, 13-21. doi:10.1016/j.jprocont.2019.08.001Pérez, 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.2011.06.004Boatwright, S., Romano, M., Mounce, S., Woodward, K., & Boxall, J. (s. f.). Optimal Sensor Placement and Leak/Burst Localisation in a Water Distribution System Using Spatially-Constrained Inverse-Distance Weighted Interpolation. doi:10.29007/37cpBlesa, J., Nejjari, F., & Sarrate, R. (2015). Robust sensor placement for leak location: analysis and design. Journal of Hydroinformatics, 18(1), 136-148. doi:10.2166/hydro.2015.021Steffelbauer, D. B., & Fuchs-Hanusch, D. (2016). Efficient Sensor Placement for Leak Localization Considering Uncertainties. Water Resources Management, 30(14), 5517-5533. doi:10.1007/s11269-016-1504-6Yoo, D., Chang, D., Song, Y., & Lee, J. (2018). Optimal Placement of Pressure Gauges for Water Distribution Networks Using Entropy Theory Based on Pressure Dependent Hydraulic Simulation. Entropy, 20(8), 576. doi:10.3390/e20080576De Schaetzen, W. B. ., Walters, G. ., & Savic, D. . (2000). Optimal sampling design for model calibration using shortest path, genetic and entropy algorithms. Urban Water, 2(2), 141-152. doi:10.1016/s1462-0758(00)00052-2Cugueró-Escofet, M. À., Puig, V., & Quevedo, J. (2017). Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network. Control Engineering Practice, 63, 1-12. doi:10.1016/j.conengprac.2017.03.003Sela Perelman, L., Abbas, W., Koutsoukos, X., & Amin, S. (2016). Sensor placement for fault location identification in water networks: A minimum test cover approach. Automatica, 72, 166-176. doi:10.1016/j.automatica.2016.06.005Carpitella, S., Carpitella, F., Certa, A., Benítez, J., & Izquierdo, J. (2018). Managing Human Factors to Reduce Organisational Risk in Industry. Mathematical and Computational Applications, 23(4), 67. doi:10.3390/mca23040067Addae, B. A., Zhang, L., Zhou, P., & Wang, F. (2019). Analyzing barriers of Smart Energy City in Accra with two-step fuzzy DEMATEL. Cities, 89, 218-227. doi:10.1016/j.cities.2019.01.043Dalvi-Esfahani, M., Niknafs, A., Kuss, D. J., Nilashi, M., & Afrough, S. (2019). Social media addiction: Applying the DEMATEL approach. Telematics and Informatics, 43, 101250. doi:10.1016/j.tele.2019.101250Quezada, L. E., López-Ospina, H. A., Palominos, P. I., & Oddershede, A. M. (2018). Identifying causal relationships in strategy maps using ANP and DEMATEL. Computers & Industrial Engineering, 118, 170-179. doi:10.1016/j.cie.2018.02.020Nilashi, M., Samad, S., Manaf, A. A., Ahmadi, H., Rashid, T. A., Munshi, A., … Hassan Ahmed, O. (2019). Factors influencing medical tourism adoption in Malaysia: A DEMATEL-Fuzzy TOPSIS approach. Computers & Industrial Engineering, 137, 106005. doi:10.1016/j.cie.2019.106005Zhang, L., Sun, X., & Xue, H. (2019). Identifying critical risks in Sponge City PPP projects using DEMATEL method: A case study of China. Journal of Cleaner Production, 226, 949-958. doi:10.1016/j.jclepro.2019.04.067Du, Y.-W., & Zhou, W. (2019). New improved DEMATEL method based on both subjective experience and objective data. Engineering Applications of Artificial Intelligence, 83, 57-71. doi:10.1016/j.engappai.2019.05.001Yazdi, M., Nedjati, A., Zarei, E., & Abbassi, R. (2020). A novel extension of DEMATEL approach for probabilistic safety analysis in process systems. Safety Science, 121, 119-136. doi:10.1016/j.ssci.2019.09.006Chen, Z., Ming, X., Zhang, X., Yin, D., & Sun, Z. (2019). A rough-fuzzy DEMATEL-ANP method for evaluating sustainable value requirement of product service system. Journal of Cleaner Production, 228, 485-508. doi:10.1016/j.jclepro.2019.04.145Wu, W.-W., & Lee, Y.-T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499-507. doi:10.1016/j.eswa.2005.12.005Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xMahmoudi, S., Jalali, A., Ahmadi, M., Abasi, P., & Salari, N. (2019). Identifying critical success factors in Heart Failure Self-Care using fuzzy DEMATEL method. Applied Soft Computing, 84, 105729. doi:10.1016/j.asoc.2019.105729Lin, K.-P., Tseng, M.-L., & Pai, P.-F. (2018). Sustainable supply chain management using approximate fuzzy DEMATEL method. Resources, Conservation and Recycling, 128, 134-142. doi:10.1016/j.resconrec.2016.11.017Vardopoulos, I. (2019). Critical sustainable development factors in the adaptive reuse of urban industrial buildings. A fuzzy DEMATEL approach. Sustainable Cities and Society, 50, 101684. doi:10.1016/j.scs.2019.101684Mirmousa, S., & Dehnavi, H. D. (2016). Development of Criteria of Selecting the Supplier by Using the Fuzzy DEMATEL Method. Procedia - Social and Behavioral Sciences, 230, 281-289. doi:10.1016/j.sbspro.2016.09.036Acuña-Carvajal, F., Pinto-Tarazona, L., López-Ospina, H., Barros-Castro, R., Quezada, L., & Palacio, K. (2019). An integrated method to plan, structure and validate a business strategy using fuzzy DEMATEL and the balanced scorecard. Expert Systems with Applications, 122, 351-368. doi:10.1016/j.eswa.2019.01.030Chou, J.-S., & Ongkowijoyo, C. S. (2019). Hybrid decision-making method for assessing interdependency and priority of critical infrastructure. International Journal of Disaster Risk Reduction, 39, 101134. doi:10.1016/j.ijdrr.2019.101134Winter, C. de, Palleti, V. R., Worm, D., & Kooij, R. (2019). Optimal placement of imperfect water quality sensors in water distribution networks. Computers & Chemical Engineering, 121, 200-211. doi:10.1016/j.compchemeng.2018.10.021Schwaller, J., & van Zyl, J. E. (2015). Modeling the Pressure-Leakage Response of Water Distribution Systems Based on Individual Leak Behavior. Journal of Hydraulic Engineering, 141(5), 04014089. doi:10.1061/(asce)hy.1943-7900.0000984Giustolisi, O., Savic, D., & Kapelan, Z. (2008). Pressure-Driven Demand and Leakage Simulation for Water Distribution Networks. Journal of Hydraulic Engineering, 134(5), 626-635. doi:10.1061/(asce)0733-9429(2008)134:5(626)EPANET 2: Users Manualhttps://epanet.es/wp-content/uploads/2012/10/EPANET_User_Guide.pdfChristodoulou, S. E., Gagatsis, A., Xanthos, S., Kranioti, S., Agathokleous, A., & Fragiadakis, M. (2013). Entropy-Based Sensor Placement Optimization for Waterloss Detection in Water Distribution Networks. Water Resources Management, 27(13), 4443-4468. doi:10.1007/s11269-013-0419-8Falatoonitoosi, E., Leman, Z., Sorooshian, S., & Salimi, M. (2013). Decision-Making Trial and Evaluation Laboratory. 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Water quality sensor placement: a multi-objective and multi-criteria approach

[EN] To satisfy their main goal, namely providing quality water to consumers, water distribution networks (WDNs) need to be suitably monitored. Only well designed and reliable monitoring data enables WDN managers to make sound decisions on their systems. In this belief, water utilities worldwide have invested in monitoring and data acquisition systems. However, good monitoring needs optimal sensor placement and presents a multi-objective problem where cost and quality are conflicting objectives (among others). In this paper, we address the solution to this multi-objective problem by integrating quality simulations using EPANET-MSX, with two optimization techniques. First, multi-objective optimization is used to build a Pareto front of non-dominated solutions relating contamination detection time and detection probability with cost. To assist decision makers with the selection of an optimal solution that provides the best trade-off for their utility, a multi-criteria decision-making technique is then used with a twofold objective: 1) to cluster Pareto solutions according to network sensitivity and entropy as evaluation parameters; and 2) to rank the solutions within each cluster to provide deeper insight into the problem when considering the utility perspectives.The clustering process, which considers features related to water utility needs and available information, helps decision makers select reliable and useful solutions from the Pareto front. Thus, while several works on sensor placement stop at multi-objective optimization, this work goes a step further and provides a reduced and simplified Pareto front where optimal solutions are highlighted. The proposed methodology uses the NSGA-II algorithm to solve the optimization problem, and clustering is performed through ELECTRE TRI. The developed methodology is applied to a very well-known benchmarking WDN, for which the usefulness of the approach is shown. The final results, which correspond to four optimal solution clusters, are useful for decision makers during the planning and development of projects on networks of quality sensors. The obtained clusters exhibit distinctive features, opening ways for a final project to prioritize the most convenient solution, with the assurance of implementing a Pareto-optimal solution.Brentan, B.; Carpitella, S.; Barros, D.; Meirelles, G.; Certa, A.; Izquierdo Sebastián, J. (2021). Water quality sensor placement: a multi-objective and multi-criteria approach. Water Resources Management. 35(1):225-241. https://doi.org/10.1007/s11269-020-02720-3S225241351Barak S, Mokfi T (2019) Evaluation and selection of clustering methods using a hybrid group mcdm. 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