Human activities both depend upon and have consequences on the environment.
Environmental risk assessment (ERA) is a process of estimating the probability and …

This chapter introduces a framework for combining scientific knowledge of physics-based
models with neural networks to advance scientific discovery. It explains termed physics …

Physics-based models are often used to study engineering and environmental systems. The
ability to model these systems is the key to achieving our future environmental sustainability …

This paper proposes a physics-guided recurrent neural network model (PGRNN) that
combines RNNs and physics-based models to leverage their complementary strengths and …

To simultaneously address the rising need of expressing uncertainties in deep learning
models along with producing model outputs which are consistent with the known scientific …

Headwater streams and wetlands are integral components of watersheds that are critical for
biodiversity, fisheries, ecosystem functions, natural resource‐based economies, and human …

Climate change is among the most serious environmental challenge facing humanity and
the ecosystems that provide the goods and services on which it relies. Climate change has …

Despite the prevalence of damming as a global disturbance to river habitats, detailed reach-
based assessments of the ecological effects of dams are lacking, particularly across large …

This work addresses human stressors and their impacts on fish assemblages at pan-
European scale by analysing single and multiple stressors and their interactions. Based on …

Given their tremendous success in commercial applications, machine learning (ML) models
are increasingly being considered as alternatives to science-based models in many …