A review of artificial neural network models for ambient air pollution prediction

By Sheen Mclean Cabaneros, John Kaiser Calautit, Ben Richard Hughes. Environmental Modelling & Software, 2019

373 citations recorded by the source corpus.

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 models entails levels of uncertainty given the black-box nature of ANNs. In this paper, a protocol by Maier et al. (2010) for ANN model development is presented and applied to assess journal papers dealing with air pollution forecasting using ANN models. The majority of the reviewed works are aimed at the long-term forecasting of outdoor PM10, PM2.5, and oxides of nitrogen, and ozone. The vast majority of the identified works utilised meteorological and source emissions predictors almost exclusively. Furthermore, ad-hoc approaches are found to be predominantly used for determining optimal model predictors, appropriate data subsets and the optimal model structure. Multilayer perceptron and ensemble-type models are predominantly implemented. Overall, the findings highlight the need for developing systematic protocols for developing powerful ANN models.

Subfields: Automotive Engineering, Environmental Engineering, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/283c877b16b7304a658f3410235a4f9a8be3457a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Yeqi Liu
2. Daoliang Li
3. Ling Yang
4. Lihua Song
5. Yingyi Chen

Citations in the corpus

• A Review of the Artificial Neural Network Models for Water Quality Prediction (2020, methodology): “Data dividing methods can be categorized into supervised and unsupervised methods [31].”