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 …
An innovative coupled model in view of wavelet transform for predicting short-term PM10 concentration
W Qiao, Y Wang, J Zhang, W Tian, Y Tian… - Journal of Environmental …, 2021 - Elsevier
Wavelet transform (WT) is an advanced preprocessing technique, which has been widely
used in PM 10 prediction. However, this technique cannot provide stable performance due …
used in PM 10 prediction. However, this technique cannot provide stable performance due …
[HTML][HTML] Identification of key volatile organic compounds in aircraft cabins and associated inhalation health risks
The identification of key VOCs during flights is important in creating a satisfactory aircraft
cabin environment. Two VOC databases for the building indoor environment (from 251 …
cabin environment. Two VOC databases for the building indoor environment (from 251 …
Water quality modelling using artificial neural network and multivariate statistical techniques
This study investigates and proposes a reduction in the number of water quality monitoring
stations, parameters and develops the best input combination for water quality modelling …
stations, parameters and develops the best input combination for water quality modelling …
Forecasting concentrations of air pollutants using support vector regression improved with particle swarm optimization: Case study in Aburrá Valley, Colombia
Air pollution is one of the most important risk factors for human health, exposure to PM 2.5 is
an important cause of death from cardiorespiratory conditions, in Aburrá Valley, an average …
an important cause of death from cardiorespiratory conditions, in Aburrá Valley, an average …
Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates …
Total suspended particulates (TSP), as a key pollutant, is a serious threat for air quality,
climate, ecosystems and human health. Therefore, measurements, prediction and …
climate, ecosystems and human health. Therefore, measurements, prediction and …
Evaluation of PM10 concentration by using Mars and XGBOOST algorithms in Iğdır Province of Türkiye
S Tırınk, B Öztürk - International Journal of Environmental Science and …, 2023 - Springer
Air pollution is extremely harmful to human health, ecosystems and the climate. In this
context, the level of particulate matter at high concentrations is one of the most important …
context, the level of particulate matter at high concentrations is one of the most important …
Improving the quantification of fine particulates (PM2. 5) concentrations in Malaysia using simplified and computationally efficient models
Air pollution assessment in urban and rural areas is really challenging due to high spatio-
temporal variability of aerosols and pollutants and the uncertainties in measurements and …
temporal variability of aerosols and pollutants and the uncertainties in measurements and …
Prediction of particular matter concentrations by developed feed-forward neural network with rolling mechanism and gray model
Particular matter (PM) due to its side effects on human health like increase the risk of lung
cancer and vision impairment has been one of the major concerns for air quality. These …
cancer and vision impairment has been one of the major concerns for air quality. These …
Investigation of PM10 prediction utilizing data mining techniques: Analyze by topic
Abstract Coarse particulate matter (PM10), the inhalable particles with an aerodynamic
diameter smaller than 10 micrometers are one of the major air pollutions that affect human …
diameter smaller than 10 micrometers are one of the major air pollutions that affect human …