- -

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

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

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

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Navarrete-López;H ...
Tamaño: 1.069Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Navarrete-López, Claudia Fernanda es_ES
dc.contributor.author Herrera Fernández, Antonio Manuel es_ES
dc.contributor.author Brentan, B. M. es_ES
dc.contributor.author Luvizotto Jr., E. es_ES
dc.contributor.author Izquierdo Sebastián, Joaquín es_ES
dc.date.accessioned 2020-04-08T05:58:39Z
dc.date.available 2020-04-08T05:58:39Z
dc.date.issued 2019-02 es_ES
dc.identifier.issn 2073-4441 es_ES
dc.identifier.uri http://hdl.handle.net/10251/140498
dc.description.abstract [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. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Water es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Water distribution systems es_ES
dc.subject Epidemiology es_ES
dc.subject Time series analysis es_ES
dc.subject Pattern recognition es_ES
dc.subject Dimension reduction es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/w11020246 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Matemática Multidisciplinar - Institut Universitari de Matemàtica Multidisciplinària es_ES
dc.description.bibliographicCitation 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/w11020246 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/w11020246 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11 es_ES
dc.description.issue 246 es_ES
dc.relation.pasarela S\378050 es_ES
dc.description.references Fecarotta, 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/resources7040073 es_ES
dc.description.references Creaco, 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/w10040466 es_ES
dc.description.references Nguyen, 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.2017.12.015 es_ES
dc.description.references Adamowski, 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.1943-5584.0000245 es_ES
dc.description.references Caiado, 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.1943-5584.0000182 es_ES
dc.description.references 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.2010.04.005 es_ES
dc.description.references Msiza, 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.3.11.1-8 es_ES
dc.description.references Tiwari, 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-2016-0004 es_ES
dc.description.references Vijayalaksmi, 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.2015.02.119 es_ES
dc.description.references Zhou, 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/ijerph13040355 es_ES
dc.description.references Cadenas, 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/en9020109 es_ES
dc.description.references Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175. doi:10.1016/s0925-2312(01)00702-0 es_ES
dc.description.references Herrera, 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:10.1080/07362994.2011.610161 es_ES
dc.description.references Khashei, 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.2010.10.015 es_ES
dc.description.references Campisi-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-y es_ES
dc.description.references Brentan, 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.2016.02.009 es_ES
dc.description.references Di 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.2014.014 es_ES
dc.description.references Scarpa, 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.1943-5452.0000639 es_ES
dc.description.references Panagopoulos, 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/s11269-011-9962-3 es_ES
dc.description.references Buchberger, 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) es_ES
dc.description.references Candelieri, A. (2017). Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection. Water, 9(3), 224. doi:10.3390/w9030224 es_ES
dc.description.references Padulano, 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/s11269-018-2140-0 es_ES
dc.description.references Bloetscher, 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/w4020367 es_ES
dc.description.references Bach, 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.2013.12.018 es_ES
dc.description.references Goltsev, 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.109.128702 es_ES
dc.description.references Danila, B., Yu, Y., Marsh, J. A., & Bassler, K. E. (2006). Optimal transport on complex networks. Physical Review E, 74(4). doi:10.1103/physreve.74.046106 es_ES
dc.description.references Herrera, 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.2012.02.005 es_ES
dc.description.references Maslov, 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.2003.06.002 es_ES
dc.description.references Lloyd, A. L., & Valeika, S. (2007). Network models in epidemiology: an overview. World Scientific Lecture Notes in Complex Systems, 189-214. doi:10.1142/9789812771582_0008 es_ES
dc.description.references Hamilton, 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.2017.08.079 es_ES
dc.description.references Bardet, J.-P., & Little, R. (2014). Epidemiology of urban water distribution systems. Water Resources Research, 50(8), 6447-6465. doi:10.1002/2013wr015017 es_ES
dc.description.references De 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/nphys3865 es_ES
dc.description.references Hamilton, 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:10.1080/09613218.2013.798142 es_ES
dc.description.references Herrera, 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-160716 es_ES
dc.description.references Padulano, 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/s11269-018-2012-7 es_ES
dc.description.references Lin, 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-z es_ES
dc.description.references Aghabozorgi, S., & Wah, T. Y. (2014). Clustering of large time series datasets. Intelligent Data Analysis, 18(5), 793-817. doi:10.3233/ida-140669 es_ES
dc.description.references Yuan, 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-150754 es_ES
dc.description.references Rasheed, F., Alshalalfa, M., & Alhajj, R. (2011). Efficient Periodicity Mining in Time Series Databases Using Suffix Trees. IEEE Transactions on Knowledge and Data Engineering, 23(1), 79-94. doi:10.1109/tkde.2010.76 es_ES
dc.description.references Schmieder, R., & Edwards, R. (2011). Fast Identification and Removal of Sequence Contamination from Genomic and Metagenomic Datasets. PLoS ONE, 6(3), e17288. doi:10.1371/journal.pone.0017288 es_ES
dc.description.references Valimaki, N., Gerlach, W., Dixit, K., & Makinen, V. (2007). Compressed suffix tree a basis for genome-scale sequence analysis. Bioinformatics, 23(5), 629-630. doi:10.1093/bioinformatics/btl681 es_ES
dc.description.references Ezkurdia, I., Juan, D., Rodriguez, J. M., Frankish, A., Diekhans, M., Harrow, J., … Tress, M. L. (2014). Multiple evidence strands suggest that there may be as few as 19 000 human protein-coding genes. Human Molecular Genetics, 23(22), 5866-5878. doi:10.1093/hmg/ddu309 es_ES
dc.description.references Bermudez-Santana, C. I. (2016). APLICACIONES DE LA BIOINFORMÁTICA EN LA MEDICINA: EL GENOMA HUMANO. ¿CÓMO PODEMOS VER TANTO DETALLE? Acta Biológica Colombiana, 21(1Supl), 249-258. doi:10.15446/abc.v21n1supl.51233 es_ES
dc.description.references Cai, L., Li, X., Ghosh, M., & Guo, B. (2009). Stability analysis of an HIV/AIDS epidemic model with treatment. Journal of Computational and Applied Mathematics, 229(1), 313-323. doi:10.1016/j.cam.2008.10.067 es_ES
dc.description.references Jackson, M., & Chen-Charpentier, B. M. (2017). Modeling plant virus propagation with delays. Journal of Computational and Applied Mathematics, 309, 611-621. doi:10.1016/j.cam.2016.04.024 es_ES
dc.description.references Brentan, B. M., Meirelles, G., Herrera, M., Luvizotto, E., & Izquierdo, J. (2017). Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models. Mathematical Problems in Engineering, 2017, 1-10. doi:10.1155/2017/6343625 es_ES
dc.description.references Bhaskaran, K., Gasparrini, A., Hajat, S., Smeeth, L., & Armstrong, B. (2013). Time series regression studies in environmental epidemiology. International Journal of Epidemiology, 42(4), 1187-1195. doi:10.1093/ije/dyt092 es_ES
dc.description.references HELFENSTEIN, U. (1991). The Use of Transfer Function Models, Intervention Analysis and Related Time Series Methods in Epidemiology. International Journal of Epidemiology, 20(3), 808-815. doi:10.1093/ije/20.3.808 es_ES
dc.description.references Herrera, M., Abraham, E., & Stoianov, I. (2016). A Graph-Theoretic Framework for Assessing the Resilience of Sectorised Water Distribution Networks. Water Resources Management, 30(5), 1685-1699. doi:10.1007/s11269-016-1245-6 es_ES
dc.description.references Jung, D., Choi, Y., & Kim, J. (2016). Optimal Node Grouping for Water Distribution System Demand Estimation. Water, 8(4), 160. doi:10.3390/w8040160 es_ES
dc.description.references Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., & Keogh, E. (2012). Experimental comparison of representation methods and distance measures for time series data. Data Mining and Knowledge Discovery, 26(2), 275-309. doi:10.1007/s10618-012-0250-5 es_ES
dc.description.references Cassisi, C., Prestifilippo, M., Cannata, A., Montalto, P., Patanè, D., & Privitera, E. (2016). Probabilistic Reasoning Over Seismic Time Series: Volcano Monitoring by Hidden Markov Models at Mt. Etna. Pure and Applied Geophysics, 173(7), 2365-2386. doi:10.1007/s00024-016-1284-1 es_ES
dc.description.references McCreight, E. M. (1976). A Space-Economical Suffix Tree Construction Algorithm. Journal of the ACM, 23(2), 262-272. doi:10.1145/321941.321946 es_ES
dc.description.references Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T. (2015). Time-series clustering – A decade review. Information Systems, 53, 16-38. doi:10.1016/j.is.2015.04.007 es_ES
dc.description.references Warren Liao, T. (2005). Clustering of time series data—a survey. Pattern Recognition, 38(11), 1857-1874. doi:10.1016/j.patcog.2005.01.025 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem