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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Enabling Resilience in Cyber-Physical-Human Water Infrastructures

Rapid urbanization and growth in urban populations have forced community-scale infrastructures (e.g., water, power and natural gas distribution systems, and transportation networks) to operate at their limits. Aging (and failing) infrastructures around the world are becoming increasingly vulnerable to operational degradation, extreme weather, natural disasters and cyber attacks/failures. These trends have wide-ranging socioeconomic consequences and raise public safety concerns. In this thesis, we introduce the notion of cyber-physical-human infrastructures (CPHIs) - smart community-scale infrastructures that bridge technologies with physical infrastructures and people. CPHIs are highly dynamic stochastic systems characterized by complex physical models that exhibit regionwide variability and uncertainty under disruptions. Failures in these distributed settings tend to be difficult to predict and estimate, and expensive to repair. Real-time fault identification is crucial to ensure continuity of lifeline services to customers at adequate levels of quality. Emerging smart community technologies have the potential to transform our failing infrastructures into robust and resilient future CPHIs.In this thesis, we explore one such CPHI - community water infrastructures. Current urban water infrastructures, that are decades (sometimes over a 100 years) old, encompass diverse geophysical regimes. Water stress concerns include the scarcity of supply and an increase in demand due to urbanization. Deterioration and damage to the infrastructure can disrupt water service; contamination events can result in economic and public health consequences. Unfortunately, little investment has gone into modernizing this key lifeline.To enhance the resilience of water systems, we propose an integrated middleware framework for quick and accurate identification of failures in complex water networks that exhibit uncertain behavior. Our proposed approach integrates IoT-based sensing, domain-specific models and simulations with machine learning methods to identify failures (pipe breaks, contamination events). The composition of techniques results in cost-accuracy-latency tradeoffs in fault identification, inherent in CPHIs due to the constraints imposed by cyber components, physical mechanics and human operators. Three key resilience problems are addressed in this thesis; isolation of multiple faults under a small number of failures, state estimation of the water systems under extreme events such as earthquakes, and contaminant source identification in water networks using human-in-the-loop based sensing. By working with real world water agencies (WSSC, DC and LADWP, LA), we first develop an understanding of operations of water CPHI systems. We design and implement a sensor-simulation-data integration framework AquaSCALE, and apply it to localize multiple concurrent pipe failures. We use a mixture of infrastructure measurements (i.e., historical and live water pressure/flow), environmental data (i.e., weather) and human inputs (i.e., twitter feeds), combined and enhanced with the domain model and supervised learning techniques to locate multiple failures at fine levels of granularity (individual pipeline level) with detection time reduced by orders of magnitude (from hours/days to minutes). We next consider the resilience of water infrastructures under extreme events (i.e., earthquakes) - the challenge here is the lack of apriori knowledge and the increased number and severity of damages to infrastructures. We present a graphical model based approach for efficient online state estimation, where the offline graph factorization partitions a given network into disjoint subgraphs, and the belief propagation based inference is executed on-the-fly in a distributed manner on those subgraphs. Our proposed approach can isolate 80% broken pipes and 99% loss-of-service to end-users during an earthquake.Finally, we address issues of water quality - today this is a human-in-the-loop process where operators need to gather water samples for lab tests. We incorporate the necessary abstractions with event processing methods into a workflow, which iteratively selects and refines the set of potential failure points via human-driven grab sampling. Our approach utilizes Hidden Markov Model based representations for event inference, along with reinforcement learning methods for further refining event locations and reducing the cost of human efforts.The proposed techniques are integrated into a middleware architecture, which enables components to communicate/collaborate with one another. We validate our approaches through a prototype implementation with multiple real-world water networks, supply-demand patterns from water utilities and policies set by the U.S. EPA. While our focus here is on water infrastructures in a community, the developed end-to-end solution is applicable to other infrastructures and community services which operate in disruptive and resource-constrained environments

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

Model-free sensor placement for water distribution networks using genetic algorithms and clustering

This paper presents a model-free methodology for the placement of pressure sensors in water distribution networks (WDNs) with the aim of performing leak detection/localization tasks. The approach is based on a custom genetic algorithm (GA) optimization scheme, which considers a population whose individuals are binary vectors encoding the network nodes with/without sensors. The optimization process pursues the minimization of a distance-based metric, computed considering the pipe distance from the possible sensors to the complete set of nodes of the network, hence removing the necessity of a hydraulic model of the WDN. The methodology is completed by means of an iterative clustering technique that seeks the enhancement of incoming individuals. The proposed methodology is tested over a well-known case study, L-TOWN from the BattLeDIM2020 challenge, in order to assess its performance.Peer ReviewedPostprint (published version

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

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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Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation

This paper presents a new data-driven method for leak localization in water distribution networks. The proposed method relies on the use of available pressure measurements in some selected internal network nodes and on the estimation of the pressure at the remaining nodes using Kriging spatial interpolation. Online leak localization is attained by comparing current pressure values with their reference values. Supported by Kriging; this comparison can be performed for all the network nodes, not only for those equipped with pressure sensors. On the one hand, reference pressure values in all nodes are obtained by applying Kriging to measurement data previously recorded under network operation without leaks. On the other hand, current pressure values at all nodes are obtained by applying Kriging to the current measured pressure values. The node that presents the maximum difference (residual) between current and reference pressure values is proposed as a leaky node candidate. Thereafter, a time horizon computation based on Bayesian reasoning is applied to consider the residual time evolution, resulting in an improved leak localization accuracy. As a data-driven approach, the proposed method does not need a hydraulic model; only historical data from normal operation is required. This is an advantage with respect to most data-driven methods that need historical data for the considered leak scenarios. Since, in practice, the obtained leak localization results will strongly depend on the number of available pressure measurements and their location, an optimal sensor placement procedure is also proposed in the paper. Three different case studies illustrate the performance of the proposed methodologies.Peer ReviewedPostprint (author's final draft

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

Robust leak localization in water distribution networks using machine learning techniques

Aplicat embargament des de la data de lectura fins el 20 de desembre de 2019This 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.Postprint (published version

Pressure sensor placement for leak localization using simulated annealing with hyperparameter optimization

© 2021 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 worksThis paper presents a machine learning method for optimal pressure sensor placement in water distribution networks. The proposed approach considers annealing meta-heuristics, and it is focused on the optimal placement of the sensors to perform leak localization. Unlike other works, this method considers a limited number of sensors to be placed and some restrictions on critical nodes that can be excluded or preselected. The approach is based on minimizing a cost function; this cost function is assigned as the leak location error, which varies depending on the subset of nodes where the sensors are assigned and the configuration of the leak location method. A leak localization technique based on k-NN classifiers was used, and during the minimization of the cost function, classifier hyperparameters were simultaneously optimized. The proposed method was tested on the Hanoi water distribution network programmed in MATLAB.Postprint (author's final draft