Abstract: Numerous applications within water resources require a robust and efficient optimization algorithm. Given that these applications involve multimodal nonconvex and discontinuous search spaces, evolutionary algorithms ...

Abstract: Machine learning (ML) techniques have been popular data-driven approaches for hydrological studies during the last few decades owing to their capability to identify complex nonlinear relationships between input and output ...

Abstract: Rainfall data provide an essential input for most hydrologic analyses and designs for effective management of water resource systems. However, in practice, missing values often occur in rainfall data that can ultimately ...

Abstract: Rainfall data provide an essential input for most hydrologic analyses and designs for effective management of water resource systems. However, in practice, missing values often occur in rainfall data that can ultimately ...