Source, fate, transport and modelling of selected emerging contaminants in the aquatic environment: Current status and future perspectives
The occurrence of emerging contaminants (ECs), such as pharmaceuticals and personal
care products (PPCPs), perfluoroalkyl and polyfluoroalkyl substances (PFASs) and …
care products (PPCPs), perfluoroalkyl and polyfluoroalkyl substances (PFASs) and …
Machine learning in natural and engineered water systems
R Huang, C Ma, J Ma, X Huangfu, Q He - Water Research, 2021 - Elsevier
Water resources of desired quality and quantity are the foundation for human survival and
sustainable development. To better protect the water environment and conserve water …
sustainable development. To better protect the water environment and conserve water …
[HTML][HTML] Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model
The computational limitations of complex numerical models have led to adoption of
statistical emulators across a variety of problems in science and engineering disciplines to …
statistical emulators across a variety of problems in science and engineering disciplines to …
Hybrid decision tree-based machine learning models for short-term water quality prediction
Water resources are the foundation of people's life and economic development, and are
closely related to health and the environment. Accurate prediction of water quality is the key …
closely related to health and the environment. Accurate prediction of water quality is the key …
[HTML][HTML] Physics-informed neural networks as surrogate models of hydrodynamic simulators
In response to growing concerns surrounding the relationship between climate change and
escalating flood risk, there is an increasing urgency to develop precise and rapid flood …
escalating flood risk, there is an increasing urgency to develop precise and rapid flood …
Quantification of interfacial interaction related with adhesive membrane fouling by genetic algorithm back propagation (GABP) neural network
Since adhesive membrane fouling is critically determined by the interfacial interaction
between a foulant and a rough membrane surface, efficient quantification of the interfacial …
between a foulant and a rough membrane surface, efficient quantification of the interfacial …
Real-time probabilistic forecasting of river water quality under data missing situation: Deep learning plus post-processing techniques
Y Zhou - Journal of Hydrology, 2020 - Elsevier
Quantifying the uncertainty of probabilistic water quality forecasting induced by missing input
data is fundamentally challenging. This study introduced a novel methodology for …
data is fundamentally challenging. This study introduced a novel methodology for …
Artificial intelligence for surface water quality monitoring and assessment: a systematic literature analysis
The goal of this paper was to conduct a systematic literature analysis on the application of
different types of artificial intelligence models in surface water quality monitoring. The …
different types of artificial intelligence models in surface water quality monitoring. The …
Prediction of long-term water quality using machine learning enhanced by Bayesian optimisation
Water quality assessment is critical to better recognise the importance of water in human
society. In this study, a new framework to predict long-term water quality is proposed by …
society. In this study, a new framework to predict long-term water quality is proposed by …
Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network
JW Yu, JS Kim, X Li, YC Jong, KH Kim, GI Ryang - Environmental Pollution, 2022 - Elsevier
Water quality forecasting can provide useful information for public health protection and
support water resources management. In order to forecast water quality more accurately, this …
support water resources management. In order to forecast water quality more accurately, this …