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

Maximum Likelihood from Incomplete Data Via the EM Algorithm

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Multilayer feedforward networks are universal approximators

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Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions

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Analysis of Incomplete Climate Data: Estimation of Mean Values and Covariance Matrices and Imputation of Missing Values

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A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities

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The M4 Competition: Results, findings, conclusion and way forward

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A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory

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Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation

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HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts

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Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations

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A novel spatiotemporal convolutional long short-term neural network for air pollution prediction

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Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach

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A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile

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Deep learning architecture for air quality predictions

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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

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Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

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Optimal division of data for neural network models in water resources applications

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Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece

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Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions

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Forecasting air quality time series using deep learning

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Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics

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Neural networks and periodic components used in air quality forecasting

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Neural Networks for Applied Sciences and Engineering

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A neural network forecast for daily average PM concentrations in Belgium

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Recursive neural network model for analysis and forecast of PM10 and PM2.5

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Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area

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A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration

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Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki

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Neural network prediction model for fine particulate matter (PM2.5) on the US–Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua)

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Evolving the neural network model for forecasting air pollution time series

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Spatial estimation of urban air pollution with the use of artificial neural network models

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Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks

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An evaluation framework for input variable selection algorithms for environmental data-driven models

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Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone

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A Deep Spatial-Temporal Ensemble Model for Air Quality Prediction

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Forecasting of the daily meteorological pollution using wavelets and support vector machine

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Prediction of 24-hour-average PM2.5 concentrations using a hidden Markov model with different emission distributions in Northern California

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An ensemble long short-term memory neural network for hourly PM2.5 concentration forecasting

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Linear and nonlinear modeling approaches for urban air quality prediction

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PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors

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Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies

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Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis

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PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization

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Integrated systems for forecasting urban meteorology, air pollution and population exposure

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Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management

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A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction

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Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification

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Neural Networks for Applied Sciences and Engineering

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Forecasting PM10 in metropolitan areas: Efficacy of neural networks

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Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using Artificial Neural Networks, Principal Component Analysis, and k-means clustering

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Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

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Procedures for estimation of modelling uncertainty in air quality assessment

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A New Formulation for Feedforward Neural Networks

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ARTIFICIAL INTELLIGENCE-BASED PREDICTION MODELS FOR ENVIRONMENTAL ENGINEERING

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Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

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Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy

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3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece

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Statistical models to assess the health effects and to forecast ground-level ozone

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Handling missing data in multivariate time series using a vector autoregressive model-imputation (VAR-IM) algorithm

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Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions

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Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

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Single imputation method of missing values in environmental pollution data sets

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Bayesian training of artificial neural networks used for water resources modeling

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Three improved neural network models for air quality forecasting

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PCA model building with missing data: New proposals and a comparative study

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Development of artificial intelligence based NO 2 forecasting models at Taj Mahal, Agra

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Improving the accuracy of prediction of PM10 pollution by the wavelet transformation and an ensemble of neural predictors

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An innovative hybrid air pollution early-warning system based on pollutants forecasting and Extenics evaluation

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Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: Case study of Hong Kong

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Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality

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A Wavelet-based Neural Network Model to Predict Ambient Air Pollutants’ Concentration

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Research and application of a novel hybrid air quality early-warning system: A case study in China

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Real‐time deployment of artificial neural network forecasting models: Understanding the range of applicability

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Hybrid Artificial Neural Network Models for Effective Prediction and Mitigation of Urban Roadside NO2 Pollution

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Improved validation framework and R-package for artificial neural network models

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Prediction of hourly ground-level PM 2.5 concentrations 3 days in advance using neural networks with satellite data in eastern China

Mao, X., Shen, T., & Feng, X. (2017) · Atmospheric Pollution Research · doi:10.1016/j.apr.2017.04.002 · Cited by 65

Prediction of particulate matter at street level using artificial neural networks coupling with chaotic particle swarm optimization algorithm

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Application of computational intelligence techniques to forecast daily PM 10 exceedances in Brunei Darussalam

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Forecasting of ozone episode days by cost-sensitive neural network methods

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A satellite-based model for estimating PM2.5 concentration in a sparsely populated environment using soft computing techniques

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Applying linear and nonlinear models for the estimation of particulate matter variability

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Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks

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Bayesian model selection applied to artificial neural networks used for water resources modeling

Kingston, G. B., Maier, H. R., & Lambert, M. F. (2008) · Water Resources Research · doi:10.1029/2007wr006155 · Cited by 40

Predicting Real-Time Roadside CO and $\hbox{NO}_{2}$ Concentrations Using Neural Networks

Zito, P., Chen, H., & Bell, M. C. (2008) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2008.928259 · Cited by 36

Prediction of nitrogen oxides emissions at the national level based on optimized artificial neural network model

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Neuro-Fuzzy approach to forecasting Ozone Episodes over the urban area of Delhi, India

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Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

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Vehicular pollution modeling using artificial neural network technique: A review

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Emissions inventory, ISCST, and neural network modelling of air pollution in Kuwait

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Nonlinear Dynamical Characterization and Prediction of Ambient Nitrogen Dioxide Concentration

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Imbalanced classification by learning hidden data structure

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Artificial neural networks prediction and optimization based on four light regions for light utilization from Synechocystis sp. PCC 6803

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