Deep learning for air pollutant concentration prediction: A review
Air pollution has become one of the critical environmental problem in the 21st century and
has attracted worldwide attentions. To mitigate it, many researchers have investigated the …
has attracted worldwide attentions. To mitigate it, many researchers have investigated the …
Artificial intelligence technologies for forecasting air pollution and human health: a narrative review
Air pollution is a major issue all over the world because of its impacts on the environment
and human beings. The present review discussed the sources and impacts of pollutants on …
and human beings. The present review discussed the sources and impacts of pollutants on …
Fourier-based type-2 fuzzy neural network: Simple and effective for high dimensional problems
The main contribution of this study is to introduce a simple and effective deep learning
Fourier-based type-2 fuzzy neural network for high-dimensional problems. The rules are …
Fourier-based type-2 fuzzy neural network for high-dimensional problems. The rules are …
Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer
Air pollution is a serious problem that affects economic development and people's health, so
an efficient and accurate air quality prediction model would help to manage the air pollution …
an efficient and accurate air quality prediction model would help to manage the air pollution …
Real time image-based air quality forecasts using a 3D-CNN approach with an attention mechanism
This study presented an image-based deep learning method to improve the recognition of
air quality from images and produce accurate multiple horizon forecasts. The proposed …
air quality from images and produce accurate multiple horizon forecasts. The proposed …
Robust echo state network with Cauchy loss function and hybrid regularization for noisy time series prediction
F Li, Y Li - Applied Soft Computing, 2023 - Elsevier
Noisy time series prediction is a hot research topic in practical applications. Echo state
networks (ESNs) have superior performance on time series prediction. However, the ill …
networks (ESNs) have superior performance on time series prediction. However, the ill …
Extraction of multi-scale features enhances the deep learning-based daily PM2. 5 forecasting in cities
Characterising the daily PM2. 5 concentration is crucial for air quality control. To govern the
status of the atmospheric environment, a novel hybrid model for PM2. 5 forecasting was …
status of the atmospheric environment, a novel hybrid model for PM2. 5 forecasting was …
Synergistic observation of FY-4A&4B to estimate CO concentration in China: combining interpretable machine learning to reveal the influencing mechanisms of CO …
B Chen, J Hu, Y Wang - npj Climate and Atmospheric Science, 2024 - nature.com
Accurately estimating the concentration of carbon monoxide (CO) with high spatiotemporal
resolution is crucial for assessing its meteorological-environmental-health impacts. Although …
resolution is crucial for assessing its meteorological-environmental-health impacts. Although …
Interpretable machine learning approaches for forecasting and predicting air pollution: A systematic review
A Houdou, I El Badisy, K Khomsi, SA Abdala… - Aerosol and Air Quality …, 2024 - aaqr.org
Many studies use machine learning to predict atmospheric pollutant levels, prioritizing
accuracy over interpretability. This systematic review will focus on reviewing studies that …
accuracy over interpretability. This systematic review will focus on reviewing studies that …
Prediction of ammonia concentration in a pig house based on machine learning models and environmental parameters
S Peng, J Zhu, Z Liu, B Hu, M Wang, S Pu - Animals, 2022 - mdpi.com
Simple Summary With the increased development of pig farming intensification, air quality
and odor emissions in pig houses are gradually attracting attention. Among them, ammonia …
and odor emissions in pig houses are gradually attracting attention. Among them, ammonia …