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

SM Cabaneros, JK Calautit, BR Hughes - Environmental Modelling & …, 2019 - Elsevier
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 …

A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance

A Masood, K Ahmad - Journal of Cleaner Production, 2021 - Elsevier
Accurate air quality forecasting is critical for systematic pollution control as well as public
health and wellness. Most of the traditional forecasting techniques have shown inconsistent …

Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation

X Li, L Peng, X Yao, S Cui, Y Hu, C You, T Chi - Environmental pollution, 2017 - Elsevier
Air pollutant concentration forecasting is an effective method of protecting public health by
providing an early warning against harmful air pollutants. However, existing methods of air …

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

Y Bai, Y Li, X Wang, J **e, C Li - Atmospheric pollution research, 2016 - Elsevier
Air quality forecasting is an effective way to protect public health by providing an early
warning against harmful air pollutants. In this paper, a model W-BPNN using wavelet …

Hydrogeochemical characterization based water resources vulnerability assessment in India's first Ramsar site of Chilka lake

D Ruidas, SC Pal, A Saha, I Chowdhuri, M Shit - Marine Pollution Bulletin, 2022 - Elsevier
A limnological site is significantly characterized by rich biological, chemical, and physical
properties of the environment and is also described as the epitome of a large aquatic …

A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2. 5 concentration forecasting

M Niu, Y Wang, S Sun, Y Li - Atmospheric environment, 2016 - Elsevier
To enhance prediction reliability and accuracy, a hybrid model based on the promising
principle of “decomposition and ensemble” and a recently proposed meta-heuristic called …

A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection

S Masmoudi, H Elghazel, D Taieb, O Yazar… - Science of the Total …, 2020 - Elsevier
Air pollution is considered one of the biggest threats for the ecological system and human
existence. Therefore, air quality monitoring has become a necessity in urban and industrial …

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

H Taheri Shahraiyni, S Sodoudi - Atmosphere, 2016 - mdpi.com
PM10 prediction has attracted special legislative and scientific attention due to its harmful
effects on human health. Statistical techniques have the potential for high-accuracy PM10 …

A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction

Z Yang, J Wang - Environmental research, 2017 - Elsevier
Air pollution in many countries is worsening with industrialization and urbanization, resulting
in climate change and affecting people's health, thus, making the work of policymakers more …

A hybrid deep learning model to forecast particulate matter concentration levels in Seoul, South Korea

G Yang, HM Lee, G Lee - Atmosphere, 2020 - mdpi.com
Both long-and short-term exposure to high concentrations of airborne particulate matter (PM)
severely affect human health. Many countries now regulate PM concentrations. Early …