Hydrologic modeling plays a very important role in assessing the seasonal water availability,
which is necessary to take decisions in water resources management. Both climate and land …

This research introduces a hybrid model for forecasting river flood events with an example of
the Mohawk River in New York. Time series analysis and artificial neural networks are …

Eight data-driven models and five data pre-processing methods were summarized; the
multiple linear regression (MLR), artificial neural network (ANN) and wavelet decomposition …

Due to limited data sources, practical situations in most developing countries favor black-box
models in real time operations. In a simple and robust approach, this study examines …

Prediction of water resources for future years takes much attention from the water resources
planners and relevant authorities. However, traditional computational models like hydrologic …

In this study, artificial intelligent (AI) techniques such as artificial neural network (ANN),
Adaptive neuro-fuzzy inference system (ANFIS) and Linear genetic programming (LGP) are …

A new hybrid model, the wavelet–bootstrap–multiple linear regression (WBMLR) is
proposed to explore the potential of wavelet analysis and bootstrap resampling techniques …

Reservoir inflow is a major component of the reservoir operations management system. It
becomes highly essential to predict the accurate reservoir inflow. The lumped models and …

Hydrological and statistical models are playing an increasing role in hydrological
forecasting, particularly for river basins with data of different temporal scales. In this study …

Reservoir inflow prediction is subject to high uncertainties in data-driven modelling. In this
study, a decomposition scheme is proposed to evaluate the individual and combined …