A review of artificial neural network models for ambient air pollution prediction
Research activity in the field of air pollution forecasting using artificial neural networks
(ANNs) has increased dramatically in recent years. However, the development of ANN …
(ANNs) has increased dramatically in recent years. However, the development of ANN …
Machine learning for streaming data: state of the art, challenges, and opportunities
Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …
associated with learning algorithms that update their models given a continuous influx of …
[HTML][HTML] The future of sensitivity analysis: an essential discipline for systems modeling and policy support
Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling.
The tremendous potential benefits of SA are, however, yet to be fully realized, both for …
The tremendous potential benefits of SA are, however, yet to be fully realized, both for …
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions
The development and application of evolutionary algorithms (EAs) and other metaheuristics
for the optimisation of water resources systems has been an active research field for over …
for the optimisation of water resources systems has been an active research field for over …
[HTML][HTML] Exploding the myths: An introduction to artificial neural networks for prediction and forecasting
Abstract Artificial Neural Networks (ANNs), sometimes also called models for deep learning,
are used extensively for the prediction of a range of environmental variables. While the …
are used extensively for the prediction of a range of environmental variables. While the …
Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US
Climate, groundwater extraction, and surface water flows have complex nonlinear
relationships with groundwater level in agricultural regions. To better understand the relative …
relationships with groundwater level in agricultural regions. To better understand the relative …
Climate adaptation as a control problem: Review and perspectives on dynamic water resources planning under uncertainty
Climate change introduces substantial uncertainty to water resources planning and raises
the key question: when, or under what conditions, should adaptation occur? A number of …
the key question: when, or under what conditions, should adaptation occur? A number of …
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 …
Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms
Groundwater level (GWL) forecasting is crucial for irrigation scheduling, water supply and
land development. Machine learning (ML)(eg, artificial neural networks) has been …
land development. Machine learning (ML)(eg, artificial neural networks) has been …
Data-driven input variable selection for rainfall–runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines
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 …
prediction. In this study, we present a novel approach for Input Variable Selection (IVS) that …