Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling

By Wenyan Wu, Graeme C. Dandy, Holger R. Maier. Environmental Modelling & Software, 2014

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A review of artificial neural network models for ambient air pollution prediction (2019, methodology): “Popular methods under this category include means and variance, minimum and maximum, analysis of variance, goodness-of-fit testing, regression and correlation analysis, and confidence interval construction (Wu et al., 2014).”
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “If those models are not structurally valid, their behavioural response to different input stimuli can be erratic and counter to physical system understanding, making them difficult to apply in practice with confidence; see Wu et al. (2014) for a discussion.”
A Review of the Artificial Neural Network Models for Water Quality Prediction (2020, methodology): “The model structure determination, an essential step in the model development, refers to the number of layers, the number of nodes in each layer and the way they connect [30], aiming to strike a balance between network complexity and generalization ability [27].”