Selecting an adequate set of inputs is a critical step for successful data-driven streamflow
prediction. In this study, we present a novel approach for Input Variable Selection (IVS) that …

While wetlands are the largest natural source of methane (CH4) to the atmosphere, they
represent a large source of uncertainty in the global CH4 budget due to the complex …

Abstract Input Variable Selection (IVS) is an essential step in the development of data-driven
models and is particularly relevant in environmental modelling. While new methods for …

A novel ensemble-based conceptual-data-driven approach (CDDA) is developed where a
data-driven model (DDM) is used to “correct” the residuals from an ensemble of hydrological …

A novel multivariate quantile-matching nesting bias correction approach is developed to
remove systematic biases in general circulation model (GCM) outputs over multiple time …

Storm water management systems depend on Intensity–Duration–Frequency (IDF) curves
as a standard design tool. However, due to climate change, the extreme precipitation …

Methane (CH4) exchange in wetlands is complex, involving nonlinear asynchronous
processes across diverse time scales. These processes and time scales are poorly …

In this study, a multiscale long short-term memory model with attention mechanism (MLSTM-
AM) is proposed to improve the accuracy of monthly precipitation forecasting. In the MLSTM …

Many hydro-climatological applications require use of General Circulation Models (GCMs)
outputs. However, the raw information as available from GCMs often contains significant …

Abstract Information theoretic measures can be used to identify nonlinear interactions
between source and target variables through reductions in uncertainty. In information …