This paper offers a comprehensive overview of machine learning (ML) methodologies and
algorithms, highlighting their practical applications in the critical domain of water resource …

It is crucial to keep an eye on the water levels in reservoirs in order for them to perform at
peak, as they are one of the, if not, the most vital part in water resource management. The …

Daily water demand forecasts are an important component of cost‐effective and sustainable
management and optimization of urban water supply systems. In this study, a method based …

Many countries are suffering from water resource constraints due to rising demands from
different water-consuming sectors and a changing climate. In some countries, such as …

This study focuses on developing a stochastic model predictive control (MPC) strategy
based on Gaussian processes (GPs) for propagating system disturbances in a receding …

This paper presents a comparison of different short-term water demand forecasting models.
The comparison regards six models that differ in terms of: forecasting technique, type of …

The present study attempts to model the spatial variability of three groundwater qualitative
parameters in Guilan Province, northern Iran, using artificial neural networks (ANNs) and …

Incomplete data with gaps is always a challenge in hydrological modeling and water
resources planning and management. Complete and reliable data is required for water …

Reliable forecasts of water demand that account for factors that drive demand are imperative
to understanding future urban water needs. The effects of meteorological dynamics and …

The success of hydraulic simulation models of water distribution networks is associated with
the ability of these models to represent real systems accurately. To achieve this, the …