MATEC Web Conf.
Volume 295, 2019Smart Underground Space and Infrastructures – Lille 2019
|Number of page(s)||5|
|Section||Smart Underground Space and Infrastructures|
|Published online||18 October 2019|
- Guo, G., and Liu, S., Short-term water demand forecast based on deep neural network. 1st International WDSA / CCWI 2018 Joint Conference, Kingston, Ontario, Canada (2018) [Google Scholar]
- Romano, M., and Kapelan, Z., Adaptive water demand forecasting for near real-ti-me management of smart water distribution systems, ENVIRON MODELL SOFTW, 60, 265-276 (2014) [CrossRef] [Google Scholar]
- Somers, M. J., and Casal, J. C., Using artificial neural networks to model nonlinearity: The case of the job satisfaction-job performance relationship, Organ. Res. Methods , 12 (3), 403-417 (2009) [CrossRef] [Google Scholar]
- Al-Zahrani, M. A., and Abo-Monasar, A., Urban residential water demand prediction based on artificial neural networks and time series models, WATER RESOUR MANAG, 29 (10), 3651-3662 (2015) [CrossRef] [Google Scholar]
- Walker, D., Creaco, E., Vamvakeridou-Lyroudia, L., Farmani, R., Kapelan, Z., and Savić, D., Forecasting domestic water consumption from smart meter readings using statistical methods and artificial neural networks, Procedia Eng., 119, 1419-1428 (2015) [CrossRef] [Google Scholar]
- Day, D., and Howe, C., Forecasting peak demand-what do we need to know? WATER SCI TECH-W SUP, 3(3), 177-184 (2003) [CrossRef] [Google Scholar]
- Farah, E., and Shahrour, I., Smart water technology for leakage detection: feedback of large-scale experimentation, ANALOG INTERGR CIRC S, 96 (2), 235-242 (2018) [CrossRef] [Google Scholar]
- Peel, M. C., Finlayson, B. L., and McMahon, T. A., Updated world map of the Köppen-Geiger climate classification, HYDROLOGY AND EARTH SYSTEM SCIENCES, 4 (2), 439-473 (2007) [CrossRef] [Google Scholar]
- Kvanli, A., Pavur, R., and Keeling, K., Concise managerial statistics. Mason, OH: South-Western Thomson Learning (2006) [Google Scholar]
- Riad, S., Mania, J., Bouchaou, L., and Najjar, Y., Rainfall-runoff model usingan artificial neural network approach, MATH COMPUT MODEL, 40 (7), 839-846 (2004) [CrossRef] [Google Scholar]
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