Improving water network management by efficient division into supply clusters

El agua es un recurso escaso que, como tal, debe ser gestionado de manera eficiente. Así, uno de los propósitos de dicha gestión debiera ser la reducción de pérdidas de agua y la mejora del funcionamiento del abastecimiento. Para ello, es necesario crear un marco de trabajo basado en un conocimiento profundo de la redes de distribución. En los casos reales, llegar a este conocimiento es una tarea compleja debido a que estos sistemas pueden estar formados por miles de nodos de consumo, interconectados entre sí también por miles de tuberías y sus correspondientes elementos de alimentación. La mayoría de las veces, esas redes no son el producto de un solo proceso de diseño, sino la consecuencia de años de historia que han dado respuesta a demandas de agua continuamente crecientes con el tiempo. La división de la red en lo que denominaremos clusters de abastecimiento, permite la obtención del conocimiento hidráulico adecuado para planificar y operar las tareas de gestión oportunas, que garanticen el abastecimiento al consumidor final. Esta partición divide las redes de distribución en pequeñas sub-redes, que son virtualmente independientes y están alimentadas por un número prefijado de fuentes. Esta tesis propone un marco de trabajo adecuado en el establecimiento de vías eficientes tanto para dividir la red de abastecimiento en sectores, como para desarrollar nuevas actividades de gestión, aprovechando esta estructura dividida. La propuesta de desarrollo de cada una de estas tareas será mediante el uso de métodos kernel y sistemas multi-agente. El spectral clustering y el aprendizaje semi-supervisado se mostrarán como métodos con buen comportamiento en el paradigma de encontrar una red sectorizada que necesite usar el número mínimo de válvulas de corte. No obstante, sus algoritmos se vuelven lentos (a veces infactibles) dividiendo una red de abastecimiento grande.Herrera Fernández, AM. (2011). Improving water network management by efficient division into supply clusters [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11233Palanci

Advanced Hydroinformatic Techniques for the Simulation and Analysis of Water Supply and Distribution Systems

[EN] This document is intended to be a presentation of the Special Issue "Advanced Hydroinformatic Techniques for the Simulation and Analysis of Water Supply and Distribution Systems". The final aim of this Special Issue is to propose a suitable framework supporting insightful hydraulic mechanisms to aid the decision-making processes of water utility managers and practitioners. Its 18 peer-reviewed articles present as varied topics as: water distribution system design, optimization of network performance assessment, monitoring and diagnosis of pressure pipe systems, optimal water quality management, and modelling and forecasting water demand. Overall, these articles explore new research avenues on urban hydraulics and hydroinformatics, showing to be of great value for both Academia and those water utility stakeholders.Herrera Fernández, AM.; Meniconi, S.; Alvisi, S.; Izquierdo Sebastián, J. (2018). Advanced Hydroinformatic Techniques for the Simulation and Analysis of Water Supply and Distribution Systems. Water. 10(4):1-7. https://doi.org/10.3390/w10040440S1710

«El Pacto Andaluz por la Naturaleza» (1985). La confluencia del movimiento campesino y el movimiento ecologista.

En los últimos años se ha desarrollado un importante debate en la Historia Ambiental acerca de lar elación entre campesinado y ecologismo, especialmente a partir de la propuesta de Guha y Martínez Alier (1997) de un Ecologismo Popular y de las críticas al mismo. En este artículo pretendemos profundizar en estas ideas a partir del análisis de la gestación y desarrollo de una de las primeras iniciativas del movimiento ecologista en España, e lPacto Andaluz por la Naturaleza, de 1985-1986.Esta iniciativa surgió de la confluencia de numerosas organizaciones ecologistas con el movimiento jornalero que representaba el Sindicato de Obreros del Campo (SOC) en la defensa de una gestión sostenible del bosque. Estas movilizaciones tuvieron como resultado la aprobación del Plan Forestal Andaluz en 1989 y la introducción, por la vía de la reclamación de mayor empleo, de valores ecologistas que venían a superar las concepciones conservacionistas hasta entonces asociadas con el ambientalismo. Esta es la historia de la confluencia entre un viejo movimiento en vías de extinción, el movimiento jornalero, y un nuevo movimiento social, el ecologista. El resultado es un buen ejemplo de la complejidad y capacidad de transformación del conflicto social.In recent years there has been an intensive debate on the relationship between peasantry and environmentalism in Environmental History. The starting point was the controversial definition of Popular Environmentalism by Ramachandra Guha and Martínez Alier (1997). In the present article we focus on these ideas through the analysis of a specific social movement, the «Andalusian Agreement for Nature» (Pacto Andaluz por la Naturaleza, 1985/1986), one of the first expressions of the Spanish green movement. The interesting feature lies in its origin. This green movement, claiming for a new sustainable use of forests, arose from the confluence of several environmentalist organizations and the traditional Peasant Union (Sindicato de Obreros del Campo). The struggles achieved a new Forest Policy (1989) adopted by the regional government, introducing new ecological values in the population beyond the traditional conservationist conception of environmentalism.This is the history of the confluence of an Old Social Movement, the peasant movement, and a New Social Movement, the green one. We consider this to be a good example for understanding the complexity and the auto‐transformation capacity of the social conflict

La protesta campesina como protesta ambiental, siglos XVIII-XX

This article analyses peasant conflicts from an environmental perspective. First, we show a general theory about environmental conflicts making a clear differentiation among environmental conflicts making a clear differentiation among environmental, environmentalist and green movement conflicts. This differentiation is accomplished using as criterion the goals of the actors in relation to both the agroecosystems sustainability and the types of discourses used in the protests. Second, we analyse the peasant use of agroecosystems and the differences between agricultural systems based on organic energy in contrast with those based on fossil energy. Finally, we identify several types of peasant environmental conflicts between 18th and 20th centuries: environmental peasant protests in Mediterranean Europe, Latin America, Asia and Africa; conflicts produced around resources like water, common goods or against pollution, Land Reform, reactions to environmental policies or defence of indigenous territories. As these examples show, attention must be paid to the environmental dimension of the peasant protest in order to understand the conflicts, although sometimes mixed with gender and class dimensions

Graph constrained label propagation on water supply networks

In many real-world applications we have at our disposal a limited number of inputs in a theoretical database with full information, and another part of experimental data with incomplete knowledge for some of their features. These are cases that can be addressed by a label propagation process. It is a widely studied approach that may acquire complexity if new constraints in the new unlabeled data that should be taken into account are found. This is the case of the membership to a group or community in graphs. The proposal is to add the Laplacian matrix as well as another different similarity measures (may be not found in the original database) in the label propagation. A kernel embedding process together with a simple label propagation algorithm will be the main tools to achieve this approach by the use of all types of available information. In order to test the functionality of this new proposal, this work introduces an experimental study of biofilm development in drinking water pipes. Then, a label propagation through pipes belonging to a complete water supply network is approached. These pipes have their own properties depending on their network location and environmental co-variables. As a result, the proposal is a suitable and efficient way to deal with practical data, based on previous theoretical studies by the constrained label propagation process introduced.Herrera Fernández, AM.; Ramos Martinez, E.; Izquierdo Sebastián, J.; Pérez García, R. (2015). Graph constrained label propagation on water supply networks. AI Communications. 28(1):47-53. doi:10.3233/AIC-140618S475328

Municipal water demand forecasting: Tools for intervention time series

This article introduces some approaches to common issues arising in real cases of water demand prediction. Occurrences of negative data gathered by the network metering system and demand changes due to closure of valves or changes in consumer behavior are considered. Artificial neural networks (ANNs) have a principal role modeling both circumstances. First, we propose the use of ANNs as a tool to reconstruct any anomalous time series information. Next, we use what we call interrupted neural networks (I-NN) as an alternative to more classical intervention ARIMA models. Besides, the use of hybrid models that combine not only the modeling ability of ARIMA to cope with the time series linear part, but also to explain nonlinearities found in their residuals, is proposed. These models have shown promising results when tested on a real database and represent a boost to the use and the applicability of ANNs.This work has been supported by project IDAWAS, DPI2009-11591, of the Direccion General de Investigacion of the Ministerio de Ciencia e Innovacion of Spain, and ACOMP/2010/146 of the Conselleria de Educacion of the Generalitat Valenciana. As well, the authors are grateful to "Aguas de Murcia" for the collaboration in this work and for the availability of the data.This work has been supported by project IDAWAS, DPI2009-11591, of the Direccion General de Investigacion of the Ministerio de Ciencia e Innovacion of Spain, and ACOMP/2010/146 of the Conseller a de Educacion of the Generalitat Valenciana. As well, the authors are grateful to "Aguas de Murcia" for the collaboration in this work and for the availability of the data.Herrera Fernández, AM.; García-Díaz, JC.; Izquierdo Sebastián, J.; Pérez García, R. (2011). Municipal water demand forecasting: Tools for intervention time series. Stochastic Analysis and Applications. 29(6):998-1007. https://doi.org/10.1080/07362994.2011.610161S9981007296Zhou, S. ., McMahon, T. ., Walton, A., & Lewis, J. (2002). Forecasting operational demand for an urban water supply zone. Journal of Hydrology, 259(1-4), 189-202. doi:10.1016/s0022-1694(01)00582-0Bougadis, J., Adamowski, K., & Diduch, R. (2005). Short-term municipal water demand forecasting. Hydrological Processes, 19(1), 137-148. doi:10.1002/hyp.5763Jain, A., & Ormsbee, L. E. (2002). Short-term water demand forecast modeling techniques-CONVENTIONAL METHODS VERSUS AI. Journal - American Water Works Association, 94(7), 64-72. doi:10.1002/j.1551-8833.2002.tb09507.xPeña, D., Tiao, G. C., & Tsay, R. S. (Eds.). (2000). A Course in Time Series Analysis. Wiley Series in Probability and Statistics. doi:10.1002/9781118032978et al. 2000 . Mining Time Series of Meteorological Variables Using Rough Sets—A Case Study, Binding Environmental Sciences and Artificial Intelligent. BESAI 2000, Germany, 7:1–8.Herrera, M., Torgo, L., Izquierdo, J., & Pérez-García, R. (2010). Predictive models for forecasting hourly urban water demand. Journal of Hydrology, 387(1-2), 141-150. doi:10.1016/j.jhydrol.2010.04.005McLeod, A. I., & Vingilis, E. R. (2005). Power Computations for Intervention Analysis. Technometrics, 47(2), 174-181. doi:10.1198/004017005000000094Box, G. E. P., & Tiao, G. C. (1975). Intervention Analysis with Applications to Economic and Environmental Problems. Journal of the American Statistical Association, 70(349), 70-79. doi:10.1080/01621459.1975.10480264Zhang, G. P., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160(2), 501-514. doi:10.1016/j.ejor.2003.08.037Zealand, C. M., Burn, D. H., & Simonovic, S. P. (1999). Short term streamflow forecasting using artificial neural networks. Journal of Hydrology, 214(1-4), 32-48. doi:10.1016/s0022-1694(98)00242-xWang, W., Gelder, P. H. A. J. M. V., Vrijling, J. K., & Ma, J. (2006). Forecasting daily streamflow using hybrid ANN models. Journal of Hydrology, 324(1-4), 383-399. doi:10.1016/j.jhydrol.2005.09.032Kneale , P. , See , L. , and Smith , A. 2001 .Towards Defining Evaluation Measures for Neural Network Forecasting Models; Proceedings of the Sixth International Conference on GeoComputation, University of Queensland, Australia.Peña, D., & Rodríguez, J. (2002). A Powerful Portmanteau Test of Lack of Fit for Time Series. Journal of the American Statistical Association, 97(458), 601-610. doi:10.1198/016214502760047122Peña, D., & Rodríguez, J. (2006). The log of the determinant of the autocorrelation matrix for testing goodness of fit in time series. Journal of Statistical Planning and Inference, 136(8), 2706-2718. doi:10.1016/j.jspi.2004.10.026LJUNG, G. M., & BOX, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303. doi:10.1093/biomet/65.2.297MONTI, A. C. (1994). A proposal for a residual autocorrelation test in linear models. Biometrika, 81(4), 776-780. doi:10.1093/biomet/81.4.77

Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework

[EN] Epidemiology-based models have shown to have successful adaptations to deal with challenges coming from various areas of Engineering, such as those related to energy use or asset management. This paper deals with urban water demand, and data analysis is based on an Epidemiology tool-set herein developed. This combination represents a novel framework in urban hydraulics. Specifically, various reduction tools for time series analyses based on a symbolic approximate (SAX) coding technique able to deal with simple versions of data sets are presented. Then, a neural-network-based model that uses SAX-based knowledge-generation from various time series is shown to improve forecasting abilities. This knowledge is produced by identifying water distribution district metered areas of high similarity to a given target area and sharing demand patterns with the latter. The proposal has been tested with databases from a Brazilian water utility, providing key knowledge for improving water management and hydraulic operation of the distribution system. This novel analysis framework shows several benefits in terms of accuracy and performance of neural network models for water demand.Navarrete-López, CF.; Herrera Fernández, AM.; Brentan, BM.; Luvizotto Jr., E.; Izquierdo Sebastián, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water. 11(246):1-17. https://doi.org/10.3390/w11020246S11711246Fecarotta, O., Carravetta, A., Morani, M., & Padulano, R. (2018). Optimal Pump Scheduling for Urban Drainage under Variable Flow Conditions. Resources, 7(4), 73. doi:10.3390/resources7040073Creaco, E., & Pezzinga, G. (2018). Comparison of Algorithms for the Optimal Location of Control Valves for Leakage Reduction in WDNs. Water, 10(4), 466. doi:10.3390/w10040466Nguyen, K. A., Stewart, R. A., Zhang, H., Sahin, O., & Siriwardene, N. (2018). Re-engineering traditional urban water management practices with smart metering and informatics. Environmental Modelling & Software, 101, 256-267. doi:10.1016/j.envsoft.2017.12.015Adamowski, J., & Karapataki, C. (2010). Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms. Journal of Hydrologic Engineering, 15(10), 729-743. doi:10.1061/(asce)he.1943-5584.0000245Caiado, J. (2010). Performance of Combined Double Seasonal Univariate Time Series Models for Forecasting Water Demand. Journal of Hydrologic Engineering, 15(3), 215-222. doi:10.1061/(asce)he.1943-5584.0000182Herrera, M., Torgo, L., Izquierdo, J., & Pérez-García, R. (2010). Predictive models for forecasting hourly urban water demand. Journal of Hydrology, 387(1-2), 141-150. doi:10.1016/j.jhydrol.2010.04.005Msiza, I. S., Nelwamondo, F. V., & Marwala, T. (2008). Water Demand Prediction using Artificial Neural Networks and Support Vector Regression. Journal of Computers, 3(11). doi:10.4304/jcp.3.11.1-8Tiwari, M., Adamowski, J., & Adamowski, K. (2016). Water demand forecasting using extreme learning machines. Journal of Water and Land Development, 28(1), 37-52. doi:10.1515/jwld-2016-0004Vijayalaksmi, D. P., & Babu, K. S. J. (2015). Water Supply System Demand Forecasting Using Adaptive Neuro-fuzzy Inference System. Aquatic Procedia, 4, 950-956. doi:10.1016/j.aqpro.2015.02.119Zhou, L., Xia, J., Yu, L., Wang, Y., Shi, Y., Cai, S., & Nie, S. (2016). Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans. International Journal of Environmental Research and Public Health, 13(4), 355. doi:10.3390/ijerph13040355Cadenas, E., Rivera, W., Campos-Amezcua, R., & Heard, C. (2016). Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model. Energies, 9(2), 109. doi:10.3390/en9020109Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175. doi:10.1016/s0925-2312(01)00702-0Herrera, M., García-Díaz, J. C., Izquierdo, J., & Pérez-García, R. (2011). Municipal Water Demand Forecasting: Tools for Intervention Time Series. Stochastic Analysis and Applications, 29(6), 998-1007. doi:10.1080/07362994.2011.610161Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664-2675. doi:10.1016/j.asoc.2010.10.015Campisi-Pinto, S., Adamowski, J., & Oron, G. (2012). Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy. Water Resources Management, 26(12), 3539-3558. doi:10.1007/s11269-012-0089-yBrentan, B. M., Luvizotto Jr., E., Herrera, M., Izquierdo, J., & Pérez-García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics, 309, 532-541. doi:10.1016/j.cam.2016.02.009Di Nardo, A., Di Natale, M., Musmarra, D., Santonastaso, G. F., Tzatchkov, V., & Alcocer-Yamanaka, V. H. (2014). Dual-use value of network partitioning for water system management and protection from malicious contamination. Journal of Hydroinformatics, 17(3), 361-376. doi:10.2166/hydro.2014.014Scarpa, F., Lobba, A., & Becciu, G. (2016). Elementary DMA Design of Looped Water Distribution Networks with Multiple Sources. Journal of Water Resources Planning and Management, 142(6), 04016011. doi:10.1061/(asce)wr.1943-5452.0000639Panagopoulos, G. P., Bathrellos, G. D., Skilodimou, H. D., & Martsouka, F. A. (2012). Mapping Urban Water Demands Using Multi-Criteria Analysis and GIS. Water Resources Management, 26(5), 1347-1363. doi:10.1007/s11269-011-9962-3Buchberger, S. G., & Nadimpalli, G. (2004). Leak Estimation in Water Distribution Systems by Statistical Analysis of Flow Readings. Journal of Water Resources Planning and Management, 130(4), 321-329. doi:10.1061/(asce)0733-9496(2004)130:4(321)Candelieri, A. (2017). Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection. Water, 9(3), 224. doi:10.3390/w9030224Padulano, R., & Del Giudice, G. (2018). Pattern Detection and Scaling Laws of Daily Water Demand by SOM: an Application to the WDN of Naples, Italy. Water Resources Management, 33(2), 739-755. doi:10.1007/s11269-018-2140-0Bloetscher, F. (2012). Protecting People, Infrastructure, Economies, and Ecosystem Assets: Water Management in the Face of Climate Change. Water, 4(2), 367-388. doi:10.3390/w4020367Bach, P. M., Rauch, W., Mikkelsen, P. S., McCarthy, D. T., & Deletic, A. (2014). A critical review of integrated urban water modelling – Urban drainage and beyond. Environmental Modelling & Software, 54, 88-107. doi:10.1016/j.envsoft.2013.12.018Goltsev, A. V., Dorogovtsev, S. N., Oliveira, J. G., & Mendes, J. F. F. (2012). Localization and Spreading of Diseases in Complex Networks. Physical Review Letters, 109(12). doi:10.1103/physrevlett.109.128702Danila, B., Yu, Y., Marsh, J. A., & Bassler, K. E. (2006). Optimal transport on complex networks. Physical Review E, 74(4). doi:10.1103/physreve.74.046106Herrera, M., Izquierdo, J., Pérez-García, R., & Montalvo, I. (2012). Multi-agent adaptive boosting on semi-supervised water supply clusters. Advances in Engineering Software, 50, 131-136. doi:10.1016/j.advengsoft.2012.02.005Maslov, S., Sneppen, K., & Zaliznyak, A. (2004). Detection of topological patterns in complex networks: correlation profile of the internet. Physica A: Statistical Mechanics and its Applications, 333, 529-540. doi:10.1016/j.physa.2003.06.002Lloyd, A. L., & Valeika, S. (2007). Network models in epidemiology: an overview. World Scientific Lecture Notes in Complex Systems, 189-214. doi:10.1142/9789812771582_0008Hamilton, I., Summerfield, A., Oreszczyn, T., & Ruyssevelt, P. (2017). Using epidemiological methods in energy and buildings research to achieve carbon emission targets. Energy and Buildings, 154, 188-197. doi:10.1016/j.enbuild.2017.08.079Bardet, J.-P., & Little, R. (2014). Epidemiology of urban water distribution systems. Water Resources Research, 50(8), 6447-6465. doi:10.1002/2013wr015017De Domenico, M., Granell, C., Porter, M. A., & Arenas, A. (2016). The physics of spreading processes in multilayer networks. Nature Physics, 12(10), 901-906. doi:10.1038/nphys3865Hamilton, I. G., Summerfield, A. J., Lowe, R., Ruyssevelt, P., Elwell, C. A., & Oreszczyn, T. (2013). Energy epidemiology: a new approach to end-use energy demand research. Building Research & Information, 41(4), 482-497. doi:10.1080/09613218.2013.798142Herrera, M., Ferreira, A. A., Coley, D. A., & de Aquino, R. R. B. (2016). SAX-quantile based multiresolution approach for finding heatwave events in summer temperature time series. AI Communications, 29(6), 725-732. doi:10.3233/aic-160716Padulano, R., & Del Giudice, G. (2018). A Mixed Strategy Based on Self-Organizing Map for Water Demand Pattern Profiling of Large-Size Smart Water Grid Data. Water Resources Management, 32(11), 3671-3685. doi:10.1007/s11269-018-2012-7Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zAghabozorgi, S., & Wah, T. Y. (2014). Clustering of large time series datasets. Intelligent Data Analysis, 18(5), 793-817. doi:10.3233/ida-140669Yuan, J., Wang, Z., Han, M., & Sun, Y. (2015). A lazy associative classifier for time series. Intelligent Data Analysis, 19(5), 983-1002. doi:10.3233/ida-150754Rasheed, F., Alshalalfa, M., & Alhajj, R. (2011). Efficient Periodicity Mining in Time Series Databases Using Suffix Trees. 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Drinking Water Distribution Systems Characteristics on Biofilm Development: A Kernel based Approach

[EN] Biofilm develops in drinking water distribution systems (DWDSs) as layers of microorganisms bound by an organic matrix and attached to pipe walls. The presence of substantial and active attached biomass can lead to a decrease in water quality by generating bad tastes and odours, operational problems, biocorrosion, and residual chlorine consume, among others problems. Recently, it has also become evident that biofilm can serve as an environmental reservoir for pathogenic microorganisms, resulting in a potential health risk for humans if left unnoticed. Various studies have been performed in relation to the influence that a number of characteristics of the DWDSs have in biofilm development. Nevertheless, their joint influence, apart from few exceptions, has been scarcely studied, due to the complexity of the community and the environment under study. This research aims to study the effect that the interaction of the physical and hydraulic conditions of the DWDSs has on biofilm development. To achieve this goal we apply Kernel methods for the study of biofilm behaviour. They give a systematic and principled approach to training learning machines. Their accuracy and simplicity to approach complex problems has been a decisive factor when choosing this form of addressing the study of biofilm behaviour in DWDSs. As a result, we claim that deeper understanding of the consequences that the interaction of the relevant hydraulic and physical factors of DWDSs have on biofilm development may be obtained. Thus, the effectiveness of the DWDSs management and the quality of the distributed water would increase.This work has been supported by project IDAWAS, DPI2009-11591, of the Dirección General de Investigación of the Ministerio de Ciencia e Innovación of Spain, and ACOMP/2011/188 of the Conselleria d’Educació of the Generalitat Valenciana.Ramos Martínez, E.; Herrera Fernández, AM.; Izquierdo Sebastián, J.; Pérez García, R. (2013). Drinking Water Distribution Systems Characteristics on Biofilm Development: A Kernel based Approach. Atiner's Conference Paper Series. AGR2013(773):5-14. http://hdl.handle.net/10251/74267S514AGR201377

Multi-Agent Approach to Biofilm Development in Water Supply Systems

[EN] The presence of regulated quantities of residual disinfectant is a usual feature in water supply systems (WSSs); nevertheless, biofilm formation persists in all of them, representing a paradigm in WSSs management due to the numerous undesirable problems associated. This study attempts to create a biofilm model based on a limited number of basic interactions between bacteria and the hydraulic and physical characteristics of the pipes by the step-wise evolution of biofilm over time. Multi-Agent Systems (MASs) are used as a modelling tool to achieve this purpose, arising as an excellent starting point for further researches. A MAS consists of a population of autonomous entities (agents, biofilm bacteria in this case) situated in a shared structured framework (environment, pipes in this case). These agents operate independently but also are able to interact with their environment, coordinating themselves with other agents. By obtaining a MAS based biofilm model, it will be possible to achieve a better understanding on any situation of interest because different research scenarios could be simulated allowing to check the hypotheses on their mechanisms and to predict how biofilm evolves in WSSs.We want to express our gratitude to the research grant (FPI), Ministerio de Economía (ref.: BES-2010-039145), Spain.Ramos Martínez, E.; Herrera Fernández, AM.; Izquierdo Sebastián, J.; Pérez García, R. (2015). Multi-Agent Approach to Biofilm Development in Water Supply Systems. Atiner's Conference Paper Series. WAT2015(1693):3-9. http://hdl.handle.net/10251/74275S39WAT2015169