Intelligent Decision Support System for Real-Time Water Demand Management

References

• A. S. Mohamed and H. H. G. Savenije, Water Demand Management: Positive Incentives, Negative Incentives or Quota Regulation?, Phys Chem Earth Pt B. 25 (2000) 251–258. doi: 10.1016/S1464-1909(00)00012-5
   Web of Science ®Google Scholar
• D. B. Brooks, An Operational Definition of Water Demand Management, Int J Water Resour D. 22 (2006) 521–528. doi: 10.1080/07900620600779699
   Web of Science ®Google Scholar
• R. A. Young, Price Elasticity of Demand for Municipal Water: A Case Study of Tucson, Arizona, Water Resour Res. 9 (1973) 599–610.
   Google Scholar
• R. H. Willsie and L. H. Pratt, Water use relationships and projection corresponding with regional growth Seattle region, Water Resour Bull. 10 (1974) 360–371. doi: 10.1111/j.1752-1688.1974.tb00575.x
   Google Scholar
• D. R. Maidment and E. Parzen, Cascade model of monthly municipal water use, Water Resour Res. 20 (1984) 15–23. doi: 10.1029/WR020i001p00015
   Web of Science ®Google Scholar
• R. B. Billings and D. E. Agthe, State-space versus multiple regression for forecasting urban water demand, Nord Hydrol. 35 (1998) 411–430.
   Google Scholar
• S. Gato, N. Jayasuriya and P. Roberts, Temperature and rainfall thresholds for base use urban water demand modeling, J Hydrol. 337 (2007) 364–376. doi: 10.1016/j.jhydrol.2007.02.014
   Web of Science ®Google Scholar
• J. Bougadis, K. Adamowski and R. Diduch, Short-term municipal water demand forecasting, Hydrol Process. 19 (2005) 137–148. doi: 10.1002/hyp.5763
   Web of Science ®Google Scholar
• M. Herrera, L. Torgo, J. Izquierdo and R. Pérez-García, Predictive models for forecasting hourly urban water demand, J Hydrol. 387 (2010) 101–140. doi: 10.1016/j.jhydrol.2010.04.005
   Web of Science ®Google Scholar
• Empresa Municipal de Aguas de Gijón (Spain). http://agua.gijon.es/. Last Access: October 30, 2015.
   Google Scholar
• D. Maidment and S. Miaou, Daily water use in nine cities, Water Resour Res. 22 (1986) 845–851. doi: 10.1029/WR022i006p00845
   Web of Science ®Google Scholar
• G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control (Holden Day, San Francisco, CA, 1970)
   Google Scholar
• A. An, C. C. Shan, N. Cercone and W. Ziarko, Discovering rules from data for water demand prediction, in Proceedings in the Workshop on Machine Learning and Expert System (1995), pp. 187–202.
   Google Scholar
• L. Shvartser, U. Shamir, and M. Feldman, Forecasting Hourly Water Demands by Pattern – Recognition Approach, J Water Res Pl-ASCE. 119 (1993) 611–627. doi: 10.1061/(ASCE)0733-9496(1993)119:6(611)
   Web of Science ®Google Scholar
• G. A. Darbellay and M. Slama, Forecasting the short-term demand for electricity – Do neural networks stand a better chance?, International Journal of Forecasting. 16 (2000) 71–83. doi: 10.1016/S0169-2070(99)00045-X
   Web of Science ®Google Scholar
• N. Lertpalangsunti, C. Chan, R. Mason, and P. Tontiwachwuthikul, A tool set for construction of hybrid intelligent forecasting systems: application for water demand prediction, Artif Intell Eng. 13 (1999) 21–42. doi: 10.1016/S0954-1810(98)00008-9
   Google Scholar
• A. Jain and L. E. Ormsbee, Short-term water demand forecasting modeling techniques-conventional versus AI, Journal AWWA. 94 (2002) 64–72.
   Web of Science ®Google Scholar
• J. Liu, H. G. Savenije, and J. Xu, Forecast of water demand in Weinan city in China using WDF-ANN model, Phys Chem Earth. 28 (2002) 219–224. doi: 10.1016/S1474-7065(03)00026-3
   Google Scholar
• M. Nasseri, A. Moeini, and M. Tabesh. Forecasting monthly urban water demand using Extenden Kalman Filter and Genetic Programming, Expert Syst Appl. 38 (2011) 7387–7395. doi: 10.1016/j.eswa.2010.12.087
   Web of Science ®Google Scholar
• L. Zhang, L. B. Jack and A. K. Nandi, Fault detection using genetic programming forecasting, Mech Syst Signal Pr. 19 (2005) 271–289. doi: 10.1016/j.ymssp.2004.03.002
   Web of Science ®Google Scholar
• D. Shrestha and D. Solomatine, Machine learning approach for estimation of prediction interval for the model output, Neural Networks. 19 (2006) 225–236. doi: 10.1016/j.neunet.2006.01.012
   PubMed Web of Science ®Google Scholar
• M. Tabesh and M. Dini, Fuzzy and neuro-fuzzy models for short-term water demand forecasting in Tehran, Iran J Sci Technol B. 33 (2009) 61–77.
   Google Scholar
• I. Msiza, F. Nelwamondo and T. Marwala, Artificial neural networks and support vector machines for water demand time series forecasting, in IEEE International Conference on Systems, Man and Cybernetics (2007), pp. 638–643.
   Google Scholar
• B. Ponte, L. Ruano, R. Pino and D. De la Fuente, The Bullwhip effect in water demand management: taming it through an artificial neural networks-based system, J Water Supply Res T 64 (2015) 290–301. doi: 10.2166/aqua.2015.087
   Web of Science ®Google Scholar
• J. Costas, B. Ponte, D. De la Fuente, R. Pino and J. Puche, Applying Goldratt's Theory of Constraints to reduce the Bullwhip Effect through agent-based modeling. Expert Syst Appl. 42 (2015) 2049–2060. doi: 10.1016/j.eswa.2014.10.022
   Web of Science ®Google Scholar
• L. Breiman, Random Forests, Mach Learn. 45 (2001) 5–32. doi: 10.1023/A:1010933404324
   Web of Science ®Google Scholar
• G. Moisen and T. Frescino, Comparing five modeling techniques for predicting forest characteristics, Ecol Model. 157 (2002) 209–225. doi: 10.1016/S0304-3800(02)00197-7
   Web of Science ®Google Scholar
• S. Moss and B. Edmonds, Sociology and Simulation: Statistical and Qualitative Cross – Validation, Am J Sociol. 110 (2005) 1095–1131. doi: 10.1086/427320
   Web of Science ®Google Scholar
• I. N. Athanasiadis, A. K. Mentes, P. A. Mitkas and Y. A. Mylopoulos, A hybrid agent-based model for estimating residential water demand, Simul-Trans Soc M S. 81 (2005) 175–187.
   Google Scholar
• J. M. Galán, A. López-Paredes, and R. Del Olmo, An agent-based model for domestic water management in Valladolid metropolitan area, Water Resour Res. 45 (2009) w05401.
   Web of Science ®Google Scholar
• E. M. Zechman, Agent-Based Modeling to Simulate Contamination Events and Evaluate Threat Management Strategies in Water Distribution Systems, Risk Anal, 31 (2011) 758–772. doi: 10.1111/j.1539-6924.2010.01564.x
   PubMed Web of Science ®Google Scholar
• M. Giuliani and A. Castelletti. Assessing the value of cooperation and information exchange in large water resources systems by agent-based optimization, Water Resour Res. 49 (2013) 3192–3296.
   Google Scholar
• J. J. Ni, L. Ren, M. H. Liu and D. Q. Zhu, A Multi-agent Dynamic Assessment Approach for Water Quality Based or Improved Q-Learning Algorithm, Math Probl Eng, (2013) ID 812032.
   Web of Science ®Google Scholar
• J. Ni, M. Liu, L. Ren, and S. X. Yang, A multiagent Q-learning-based optimal allocation approach for urban water resource management system, IEEE T Autom Sci Eng. 11 (2014) 204–214. doi: 10.1109/TASE.2012.2229978
   Web of Science ®Google Scholar
• C. S. Karavas, G. Kyriakarakos, K. G. Arvanitis and G. Papadakis, A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous polygeneration microgrids. Energ Convers Manage. 103 (2015) 166–179. doi: 10.1016/j.enconman.2015.06.021
   Web of Science ®Google Scholar
• S. Makridakis, Accuracy measures: theoretical and practical concerns, Int J Forecasting. 9 (1993) 527–529. doi: 10.1016/0169-2070(93)90079-3
   Web of Science ®Google Scholar
• B. Ponte, D. De la Fuente, R. Pino and R. Rosillo, Real-time water demand forecasting system through an agent-based architecture, Int J Bio-Inspir Com. 7 (2015) 147–156. doi: 10.1504/IJBIC.2015.069559
   Web of Science ®Google Scholar