Optimal sensor placement for classifier-based leak localization in drinking water networks

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a sensor placement method for classifier-based leak localization in Water Distribution Networks. The proposed approach consists in applying a Genetic Algorithm to decide the sensors to be used by a classifier (based on the k-Nearest Neighbor approach). The sensors are placed in an optimal way maximizing the accuracy of the leak localization. The results are illustrated by means of the application to the Hanoi District Metered Area and they are compared to the ones obtained by the Exhaustive Search Algorithm. A comparison with the results of a previous optimal sensor placement method is provided as well.Postprint (author's final draft

Pressure sensor placement for leak localization in water distribution networks using information theory

This paper presents a method for optimal pressure sensor placement in water distribution networks using information theory. The criterion for selecting the network nodes where to place the pressure sensors was that they provide the most useful information for locating leaks in the network. Considering that the node pressures measured by the sensors can be correlated (mutual information), a subset of sensor nodes in the network was chosen. The relevance of information was maximized, and information redundancy was minimized simultaneously. The selection of the nodes where to place the sensors was performed on datasets of pressure changes caused by multiple leak scenarios, which were synthetically generated by simulation using the EPANET software application. In order to select the optimal subset of nodes, the candidate nodes were ranked using a heuristic algorithm with quadratic computational cost, which made it time-efficient compared to other sensor placement algorithms. The sensor placement algorithm was implemented in MATLAB and tested on the Hanoi network. It was verified by exhaustive analysis that the selected nodes were the best combination to place the sensors and detect leaksPeer ReviewedPostprint (published version

Sensor placement for classifier-based leak localization in water distribution networks using hybrid feature selection

This paper presents a sensor placement approach for classifier-based leak localization in water distribution networks. The proposed method is based on a hybrid feature selection algorithm that combines the use of a filter based on relevancy and redundancy with a wrapper based on genetic algorithms. This algorithm is applied to data generated by hydraulic simulation of the considered water distribution network and it determines the optimal location of a prespecified number of pressure sensors to be used by a leak localization method based on pressure models and classifiers proposed in previous works by the authors. The method is applied to a small-size simplified network (Hanoi) to better analyze its computational performance and to a medium-size network (Limassol) to demonstrate its applicability to larger real-size networks.Peer ReviewedPostprint (author's final draft

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. 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(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. Research Journal of Applied Sciences, Engineering and Technology, 5(13), 3476-3480. doi:10.19026/rjaset.5.4475OPRICOVIC, S., & TZENG, G.-H. (2003). DEFUZZIFICATION WITHIN A MULTICRITERIA DECISION MODEL. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(05), 635-652. doi:10.1142/s0218488503002387Sara, J., Stikkelman, R. M., & Herder, P. M. (2015). Assessing relative importance and mutual influence of barriers for CCS deployment of the ROAD project using AHP and DEMATEL methods. International Journal of Greenhouse Gas Control, 41, 336-357. doi:10.1016/j.ijggc.2015.07.008Alperovits, E., & Shamir, U. (1977). Design of optimal water distribution systems. Water Resources Research, 13(6), 885-900. doi:10.1029/wr013i006p00885Walski, T., Bezts, W., Posluszny, E. T., Weir, M., & Whitman, B. E. (2006). Modeling leakage reduction through pressure control. Journal - American Water Works Association, 98(4), 147-155. doi:10.1002/j.1551-8833.2006.tb07642.xZheng, F., Du, J., Diao, K., Zhang, T., Yu, T., & Shao, Y. (2018). Investigating Effectiveness of Sensor Placement Strategies in Contamination Detection within Water Distribution Systems. Journal of Water Resources Planning and Management, 144(4), 06018003. doi:10.1061/(asce)wr.1943-5452.0000919Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2014). Water Distribution System Computer-Aided Design by Agent Swarm Optimization. Computer-Aided Civil and Infrastructure Engineering, 29(6), 433-448. doi:10.1111/mice.1206

Leak localization in water distribution networks using a mixed model-based/data-driven approach

“The final publication is available at Springer via http://dx.doi.org/10.1016/j.conengprac.2016.07.006”This paper proposes a new method for leak localization in water distribution networks (WDNs). In a first stage, residuals are obtained by comparing pressure measurements with the estimations provided by a WDN model. In a second stage, a classifier is applied to the residuals with the aim of determining the leak location. The classifier is trained with data generated by simulation of the WDN under different leak scenarios and uncertainty conditions. The proposed method is tested both by using synthetic and experimental data with real WDNs of different sizes. The comparison with the current existing approaches shows a performance improvement.Peer ReviewedPostprint (author's final draft

10th IFAC Symposium for Fault Detection, Supervision and Safety for Technical Processes

Producción CientíficaThis paper addresses the problem of leak localization in water distribution networks (WDN) using Fisher Discriminant Analysis (FDA). First, the paper introduces how FDA can be used for leak localization using the information of pressure measurements from the sensors available in the WDN. Then, the problem of sensor placement is considered when the proposed leak localization based on FDA is used. The proposed leak localization and sensor placement approaches based on FDA will be used using a well-known WDN case study.Este trabajo forma parte del proyecto de investigación: MINECO/FEDER: DPI2015-67341-C2-2-R

Topological analysis of water distribution networks for optimal leak localization

This paper introduces two methodologies to provide an optimum sensor deployment layout, one based on a model-based approach and the other entirely data-driven. The first method is formulated as an integer optimization problem, an optimization criterion consisting of minimizing the average topological distance. The second method is a new methodology to provide an optimum sensor placement regarding how many sensors to install without using hydraulic information but just exploiting the knowledge of the topology of the Water Distribution Networks. The method uses the Girvan-Newman clustering algorithm to ensure complete coverage of the network and the study of the installation of pressure sensors in the central nodes of each group, selected according to different metrics of topological centrality. The approach is illustrated in the Modena network. © 2023 Institute of Physics Publishing. All rights reserved.Postprint (published version

Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network

Water distribution networks are large complex systems affected by leaks, which often entail high costs and may severely jeopardise the overall water distribution performance. Successful leak location is paramount in order to minimize the impact of these leaks when occurring. Sensor placement is a key issue in the leak location process, since the overall performance and success of this process highly depends on the choice of the sensors gathering data from the network. Common problems when isolating leaks in large scale highly gridded real water distribution networks include leak mislabelling and the obtention of large number of possible leak locations. This is due to similarity of leak effect in the measurements, which may be caused by topological issues and led to incomplete coverage of the whole network. The sensor placement strategy may minimize these undesired effects by setting the sensor placement optimisation problem with the appropriate assumptions (e.g. geographically cluster alike leak behaviors) and by taking into account real aspects of the practical application, such as the acceptable leak location distance. In this paper, a sensor placement methodology considering these aspects and a general sensor distribution assessment method for leak diagnosis in water distribution systems is presented and exemplified with a small illustrative case study. Finally, the proposed method is applied to two real District Metered Areas (DMAs) located within the Barcelona water distribution network.Peer ReviewedPostprint (author's final draft

Clustering techniques applied to sensor placement for leak detection and location in water distribution networks

This work presents an optimization strategy that maximizes the leak locatability performance of water distribution networks (WDN). The goal is to characterize and determine a sensor configuration that guarantees a maximum degree of locatability while the sensor configuration cost satisfies a budgetary constraint. The method is based on pressure sensitivity matrix analysis and an exhaustive search strategy. In order to reduce the size and the complexity of the problem the present work proposes to combine this methodology with clustering techniques. The strategy developed in this work is successfully applied to determine the optimal set of pressure sensors that should be installed in a district metered area (DMA) in the Barcelona WDN.Peer ReviewedPostprint (published version

Robust leak localization in water distribution networks using machine learning techniques

This PhD thesis presents a methodology to detect, estimate and localize water leaks (with the main focus in the localization problem) in water distribution networks using hydraulic models and machine learning techniques. The actual state of the art is introduced, the theoretical basis of the machine learning techniques applied are explained and the hydraulic model is also detailed. The whole methodology is presented and tested into different water distribution networks and district metered areas based on simulated and real case studies and compared with published methods. The focus of the contributions is to bring more robust methods against the uncertainties that effects the problem of leak detection, by dealing with them using the self-similarity to create features monitored by the change detection technique intersection-of-confidence-interval, and the leak localization where the problem is tackled using machine learning techniques. By using those techniques, it is expected to learn the leak behavior considering their uncertainty to be used in the diagnosis stage after the training phase. One method for the leak detection problem is presented that is able to estimate the leak size and the time that the leak has been produced. This method captures the normal, leak-free, behavior and contrast it with the new measurements in order to evaluate the state of the network. If the behavior is not normal check if it is due to a leak. To have a more robust leak detection method, a specific validation is designed to operate specifically with leaks and in the temporal region where the leak is most apparent. A methodology to extent the current model-based approach to localize water leaks by means of classifiers is proposed where the non-parametric k-nearest neighbors classifier and the parametric multi-class Bayesian classifier are proposed. A new data-driven approach to localize leaks using a multivariate regression technique without the use of hydraulic models is also introduced. This method presents a clear benefit over the model-based technique by removing the need of the hydraulic model despite of the topological information is still required. Also, the information of the expected leaks is not required since information of the expected hydraulic behavior with leak is exploited to find the place where the leak is more suitable. This method has a good performance in practice, but is very sensitive to the number of sensor in the network and their sensor placement. The proposed sensor placement techniques reduce the computational load required to take into account the amount of data needed to model the uncertainty compared with other optimization approaches while are designed to work with the leak localization problem. More precisely, the proposed hybrid feature selection technique for sensor placement is able to work with any method that can be evaluated with confusion matrix and still being specialized for the leak localization task. This last method is good for a few sensors, but lacks of precision when the number of sensors to place is large. To overcome this problem an incremental sensor placement is proposed which is better for a larger number of sensors to place but worse when the number is small.Aquesta tesi presenta una nova metodologia per a localització de fuites en xarxes de distribució d'aigua potable. Primer s'ha revisat l'estat del art actual i les bases teòriques tant de les tècniques de machine learning utilitzades al llarg de la tesi com els mètodes existents de localització de fuites. La metodologia presentada s'ha provat en diferents xarxes d'aigua simulades i reals, comparant el resultats amb altres mètodes publicats. L'objectiu principal de la contribució aportada és el de desenvolupar mètodes més robustos enfront les incerteses que afecten a la localització de fuites. En el cas de la detecció i estimació de la magnitud de la fuita, s'utilitza la tècnica self-similarity per crear els indicadors es monitoritzen amb la tècnica de detecció de canvis ("intersection-of-confidence-intervals"). En el cas de la localització de les fuites, s'han fet servir les tècniques de classificadors i interpoladors provinents del machine learning. A l'utilitzar aquestes tècniques s'espera captar el comportament de la fuita i de la incertesa per aprendre i tenir-ho en compte en la fase de la localització de la fuita. El mètode de la detecció de fallades proposat és capaç d'estimar la magnitud de la fuita i l'instant en que s'ha produït. Aquest mètode captura el comportament normal, sense fuita, i el contrasta amb les noves mesures per avaluar l'estat de la xarxa. En el cas que el comportament no sigui el normal, es procedeix a comprovar si això és degut a una fuita. Per tenir una mètode de detecció més robust, es fa servir una capa de validació especialment dissenyada per treballar específicament amb fuites i en la regió temporal en que la fuita és més evident. Per tal de millorar l'actual metodologia de localització de fuites mitjançant models hidràulics s'ha proposat l'ús de classificadors. Per una banda es proposa el classificador no paramètric k-nearest neighbors i per l'altre banda el classificador Bayesià paramètric per múltiples classes. Finalment, s'ha desenvolupat un nou mètode de localització de fuites basat en models de dades mitjançant la regressió de múltiples paràmetres sense l'ús del model hidràulic de la xarxa. Finalment, s'ha tractat el problema de la col·locació de sensors. El rendiment de la localització de fuites està relacionada amb la col·locació de sensors i és particular per a cada mètode de localització. Amb l'objectiu de maximitzar el rendiment dels mètodes de localització de fuites presentats anteriorment, es presenten i avaluen tècniques de col·locació de sensors específicament dissenyats ja que el problema de combinatòria no es pot manejar intentant cada possible combinació de sensors a part de les xarxes més petites amb pocs sensors per instal·lar. Aquestes tècniques de col·locació de sensors exploten el potencial de les tècniques de selecció de variables per tal de realitzar la tasca desitjada.Esta tesis doctoral presenta una nueva metodología para detectar, estimar el tamaño y localizar fugas de agua (donde el foco principal está puesto en el problema de la localización de fugas) en redes de distribución de agua potable. La tesis presenta una revisión del estado actual y las bases de las técnicas de machine learning que se aplican, así como una explicación del modelo hidráulico de las redes de agua. El conjunto de la metodología se presenta y prueba en diferentes redes de distribución de agua y sectores de consumo con casos de estudio simulados y reales, y se compara con otros métodos ya publicados. La contribución principal es la de desarrollar métodos más robustos frente a la incertidumbre de los datos. En la detección de fugas, la incertidumbre se trata con la técnica del self-similarity para la generación de indicadores que luego son monitoreados per la técnica de detección de cambios conocida como intersection-of-confidece-interval. En la localización de fugas el problema de la incertidumbre se trata con técnicas de machine learning. Al utilizar estas técnicas se espera aprender el comportamiento de la fuga y su incertidumbre asociada para tenerlo en cuenta en la fase de diagnóstico. El método presentado para la detección de fugas tiene la habilidad de estimar la magnitud y el instante en que la fuga se ha producido. Este método captura el comportamiento normal, sin fugas, del sistema y lo contrasta con las nuevas medidas para evaluar el estado actual de la red. En el caso de que el comportamiento no sea el normal, se comprueba si es debido a la presencia de una fuga en el sistema. Para obtener un método de detección más robusto, se considera una capa de validación especialmente diseñada para trabajar específicamente con fugas y durante el periodo temporal donde la fuga es más evidente. Esta técnica se compara con otras ya publicadas proporcionando una detección más fiable, especialmente en el caso de fugas pequeñas, al mismo tiempo que proporciona más información que puede ser usada en la fase de la localización de la fuga permitiendo mejorarla. El principal problema es que el método es más lento que los otros métodos analizados. Con el fin de mejorar la actual metodología de localización de fugas mediante modelos hidráulicos, se propone la utilización de clasificadores. Concretamente, se propone el clasificador no paramétrico k-nearest neighbors y el clasificador Bayesiano paramétrico para múltiples clases. La propuesta de localización de fugas mediante modelos hidráulicos y clasificadores permite gestionar la incertidumbre de los datos mejor para obtener un diagnóstico de la localización de la fuga más preciso. El principal inconveniente recae en el coste computacional, aunque no se realiza en tiempo real, de los datos necesarios por el clasificador para aprender correctamente la dispersión de los datos. Además, el método es muy dependiente de la calidad del modelo hidráulico de la red. En el campo de la localización de fugas, se a propuesto un nuevo método de localización de fugas basado en modelos de datos mediante la regresión de múltiples parámetros sin el uso de modelo hidráulico. Este método presenta un claro beneficio respecto a las técnicas basadas en modelos hidráulicos ya que prescinde de su uso, aunque la información topológica de la red es aún necesaria. Además, la información del comportamiento de la red para cada fuga no es necesario, ya que el conocimiento del efecto hidráulico de una fuga en un determinado punto de la red es utilizado para la localización. Este método ha dado muy buenos resultados en la práctica, aunque es muy sensible al número de sensores y a su colocación en la red. Finalmente, se trata el problema de la colocación de sensores. El desempeño de la localización de fugas está ligado a la colocación de los sensores y es particular para cada método. Con el objetivo de maximizar el desempeño de los métodos de localización de fugas presentados, técnicas de colocación de sensores específicamente diseñados para ellos se han presentado y evaluado. Dado que el problema de combinatoria que presenta no puede ser tratado analizando todas las posibles combinaciones de sensores excepto en las redes más pequeñas con unos pocos sensores para instalar. Estas técnicas de colocación de sensores explotan el potencial de las técnicas de selección de variables para realizar la tarea deseada. Las técnicas de colocación de sensores propuestas reducen la carga computacional, requerida para tener en cuenta todos los datos necesarios para modelar bien la incertidumbre, comparado con otras propuestas de optimización al mismo tiempo que están diseñadas para trabajar en la tarea de la localización de fugas. Más concretamente, la propuesta basada en la técnica híbrida de selección de variables para la colocación de sensores es capaz de trabajar con cualquier técnica de localización de fugas que se pueda evaluar con la matriz de confusión y ser a la vez óptimo. Este método es muy bueno para la colocación de sensores, pero el rendimiento disminuye a medida que el número de sensores a colocar crece. Para evitar este problema, se propone método de colocación de sensores de forma incremental que presenta un mejor rendimiento para un número alto de sensores a colocar, aunque no es tan eficaz con pocos sensores a colocar