- -

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 completo del ítem

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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/140498

Ficheros en el ítem

Thumbnail
Nombre: Navarrete-López;H ...
Tamaño: 1.069Mb
Formato: PDF
Descripción: Versión editorial
Abrir/Preview

Metadatos del ítem

Título: Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework
Autor: Navarrete-López, Claudia Fernanda Herrera Fernández, Antonio Manuel Brentan, B. M. Luvizotto Jr., E. Izquierdo Sebastián, Joaquín
Entidad UPV: Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada
Universitat Politècnica de València. Instituto Universitario de Matemática Multidisciplinar - Institut Universitari de Matemàtica Multidisciplinària
Fecha difusión:
Resumen:
[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 ...[+]
Palabras clave: Water distribution systems , Epidemiology , Time series analysis , Pattern recognition , Dimension reduction
Derechos de uso: Reconocimiento (by)
Fuente:
Water. (issn: 2073-4441 )
DOI: 10.3390/w11020246
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/w11020246
Tipo: Artículo

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

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

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 [+]
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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)

Candelieri, A. (2017). Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection. Water, 9(3), 224. doi:10.3390/w9030224

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

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

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

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

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

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

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

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

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

Bardet, J.-P., & Little, R. (2014). Epidemiology of urban water distribution systems. Water Resources Research, 50(8), 6447-6465. doi:10.1002/2013wr015017

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

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

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

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

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

Aghabozorgi, S., & Wah, T. Y. (2014). Clustering of large time series datasets. Intelligent Data Analysis, 18(5), 793-817. doi:10.3233/ida-140669

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

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

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

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

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

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

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

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

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

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

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

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

Jung, D., Choi, Y., & Kim, J. (2016). Optimal Node Grouping for Water Distribution System Demand Estimation. Water, 8(4), 160. doi:10.3390/w8040160

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

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

McCreight, E. M. (1976). A Space-Economical Suffix Tree Construction Algorithm. Journal of the ACM, 23(2), 262-272. doi:10.1145/321941.321946

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

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

[-]

recommendations

 

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

Mostrar el registro completo del ítem