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 …
Review of surrogate modeling in water resources
Surrogate modeling, also called metamodeling, has evolved and been extensively used
over the past decades. A wide variety of methods and tools have been introduced for …
over the past decades. A wide variety of methods and tools have been introduced for …
[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 …
Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling
S Razavi - Environmental Modelling & Software, 2021 - Elsevier
Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL),
have created tremendous excitement and opportunities in the earth and environmental …
have created tremendous excitement and opportunities in the earth and environmental …
Application of ANN technique to predict the performance of solar collector systems-A review
HK Ghritlahre, RK Prasad - Renewable and Sustainable Energy Reviews, 2018 - Elsevier
The solar collector is the heart of any solar energy collection system designed for operation
in the low to medium temperature ranges. So, an efficient design of solar collector system …
in the low to medium temperature ranges. So, an efficient design of solar collector system …
Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models
Wind energy, which is clean, inexhaustible and free, has been used to mitigate the crisis of
conventional resource depletion. However, wind power is difficult to implement on a large …
conventional resource depletion. However, wind power is difficult to implement on a large …
Extreme learning machine and its applications
S Ding, X Xu, R Nie - Neural Computing and Applications, 2014 - Springer
Recently, a novel learning algorithm for single-hidden-layer feedforward neural networks
(SLFNs) named extreme learning machine (ELM) was proposed by Huang et al. The …
(SLFNs) named extreme learning machine (ELM) was proposed by Huang et al. The …
Protocol for develo** ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling
Abstract The application of Artificial Neural Networks (ANNs) in the field of environmental
and water resources modelling has become increasingly popular since early 1990s. Despite …
and water resources modelling has become increasingly popular since early 1990s. Despite …
Artificial neural networks for short-term load forecasting in microgrids environment
The adaptation of energy production to demand has been traditionally very important for
utilities in order to optimize resource consumption. This is especially true also in microgrids …
utilities in order to optimize resource consumption. This is especially true also in microgrids …
A comparison of regularization techniques in deep neural networks
I Nusrat, SB Jang - Symmetry, 2018 - mdpi.com
Artificial neural networks (ANN) have attracted significant attention from researchers
because many complex problems can be solved by training them. If enough data are …
because many complex problems can be solved by training them. If enough data are …