Conventional machine learning models have been widely used for reservoir inflow and
rainfall prediction. Nowadays, researchers focus on a new computing architecture in the …

Monthly streamflow prediction is very important for many hydrological applications in
providing information for optimal use of water resources. In this study, the prediction …

Daily streamflow prediction is important for flood warning, navigation, sediment control,
reservoir operations and environmental protection. The current paper examines the …

Accurate and reliable prediction of streamflow is vital to the optimization of water resources
management, reservoir flood operations, catchment, and urban water management. In this …

For accurate estimation of streamflow of a mountainous river basin, a novel hybrid method is
developed in this study, where gradient-based optimization (GBO) algorithm is employed to …

Forecasting water level is an extremely important task as it allows to mitigate the effects of
floods, reduce and prevent disasters. Physically based models often give good results but …

The applicability of four machine learning (ML) methods, ANFIS-PSO, ANFIS-FCM, MARS
and M5Tree, together with multi model simple averaging (MM-SA) ensemble method, is …

From a watershed management perspective, streamflow need to be predicted accurately
using simple, reliable, and cost-effective tools. Present study demonstrates the first …

This study investigates the potential of two evolutionary neuro-fuzzy inference systems,
adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (ANFIS …

Accurate short-term rainfall–runoff prediction is essential for flood mitigation and safety of
hydraulic structures and infrastructures. This study investigates the capability of four …