Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …

The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …

Nowadays, mathematical models of hydrological systems are used routinely to guide
decision making in diverse subjects, such as: environmental and risk assessments, design …

Concerns over groundwater depletion and ecosystem degradation have led to the
incorporation of the concept of groundwater sustainability as a groundwater policy …

Hydrodynamic models are commonly used to understand flood risk and inform flood
management decisions. However, their high computational cost can impose practical limits …

India is facing the worst water crisis in its history and major Indian cities which accommodate
about 50% of its population will be among highly groundwater stressed cities by 2020. In …

Sea level rise has already caused more frequent and severe coastal flooding and this trend
will likely continue. Flood prediction is an essential part of a coastal city's capacity to adapt to …

Data assimilation for high-dimensional parameter joint inversion of multiple time-varying
source strength and hydraulic conductivity fields can be computationally intensive as a large …

The most common index for representing structural condition of the pavement is the
structural number. The current procedure for determining structural numbers involves …

Structural, parametric and measurement uncertainties are inherent in watershed hydrology
models. Among these, structural uncertainty results from simplified representation of reality …