|
|
|
|
|
Paolo Reggiani, Amal Talbi and Ezio Todini
In Water Resources Planning and Management, decision makers, although unsure of future outcomes, must take the most reliable and assuring decisions. Deterministic and probabilistic prediction techniques, combined with optimization tools, have been widely...
ver más
|
|
|
|
|
|
|
|
|
|
|
Luigi Bertoli, Donata Balzarolo and Ezio Todini
Rational Water Resources Management requires effective collaboration between decision-makers involved in the operational management of water resources and scientists, who can allow them to operate in an informed manner through forecasting and decision-ma...
ver más
|
|
|
|
|
|
|
|
|
|
|
Amos O. Anele, Ezio Todini, Yskandar Hamam and Adnan M. Abu-Mahfouz
In a previous paper, a number of potential models for short-term water demand (STWD) prediction have been analysed to find the ones with the best fit. The results obtained in Anele et al. (2017) showed that hybrid models may be considered as the accurate...
ver más
|
|
|
|
|
|
|
|
|
|
|
Amos O. Anele, Yskandar Hamam, Adnan M. Abu-Mahfouz and Ezio Todini
The stochastic nature of water consumption patterns during the day and week varies. Therefore, to continually provide water to consumers with appropriate quality, quantity and pressure, water utilities require accurate and appropriate short-term water de...
ver más
|
|
|
|
|
|
|
|
|
|
|
Silvia Barbetta, Gabriele Coccia, Tommaso Moramarco, Ezio Todini
Pág. 1 - 25
This work presents the application of the multi-temporal approach of the Model Conditional Processor (MCP-MT) for predictive uncertainty (PU) estimation in the Godavari River basin, India. MCP-MT is developed for making probabilistic Bayesian decision. I...
ver más
|
|
|
|
|
|