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 …
Intelligent modeling strategies for forecasting air quality time series: A review
In recent years, the deterioration of air quality, the frequent events of the air contaminants,
and the health impacts from that have caused continuous attention by the government and …
and the health impacts from that have caused continuous attention by the government and …
PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data
In recent years, air pollution has become an important public health concern. The high
concentration of fine particulate matter with diameter less than 2.5 µm (PM2. 5) is known to …
concentration of fine particulate matter with diameter less than 2.5 µm (PM2. 5) is known to …
[HTML][HTML] Spatiotemporal prediction of air quality based on LSTM neural network
D Seng, Q Zhang, X Zhang, G Chen, X Chen - Alexandria Engineering …, 2021 - Elsevier
Accurate monitoring of air quality is of great importance to our daily life. By predicting the air
quality in advance, we can make timely warnings and defenses to minimize the threat to life …
quality in advance, we can make timely warnings and defenses to minimize the threat to life …
An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2. 5 concentration in urban environment
This study proposes a new model for the spatiotemporal prediction of PM 2.5 concentration
at hourly and daily time intervals. It has been constructed on a combination of three …
at hourly and daily time intervals. It has been constructed on a combination of three …
Air pollution prediction using long short-term memory (LSTM) and deep autoencoder (DAE) models
Many countries worldwide have poor air quality due to the emission of particulate matter (ie,
PM10 and PM2. 5), which has led to concerns about human health impacts in urban areas …
PM10 and PM2. 5), which has led to concerns about human health impacts in urban areas …
A systematic literature review of deep learning neural network for time series air quality forecasting
Rapid progress of industrial development, urbanization and traffic has caused air quality
reduction that negatively affects human health and environmental sustainability, especially …
reduction that negatively affects human health and environmental sustainability, especially …
Weather forecasting for renewable energy system: a review
Energy crisis and climate change are the major concerns which has led to a significant
growth in the renewable energy resources which includes mainly the solar and wind power …
growth in the renewable energy resources which includes mainly the solar and wind power …
Deep spatio-temporal graph network with self-optimization for air quality prediction
The environment and development are major issues of general concern. After much
suffering from the harm of environmental pollution, human beings began to pay attention to …
suffering from the harm of environmental pollution, human beings began to pay attention to …
A hybrid CNN-GRU model for predicting soil moisture in maize root zone
Soil water content in maize root zone is the main basis of irrigation decision-making.
Therefore, it is important to predict the soil water content at different depths in maize root …
Therefore, it is important to predict the soil water content at different depths in maize root …