A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science
Air pollution is a global geo-hazard with significant implications, including deterioration of
health and premature death. Climatic variables such as temperature, rainfall, wind, and …
health and premature death. Climatic variables such as temperature, rainfall, wind, and …
Predicting the quality of air with machine learning approaches: Current research priorities and future perspectives
The spiraling growth of the world's population and unregulated urbanization have resulted in
many environmental problems, including poor quality of air, which is associated with a wide …
many environmental problems, including poor quality of air, which is associated with a wide …
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] An LSTM-based aggregated model for air pollution forecasting
During the past few years, severe air-pollution problem has garnered worldwide attention
due to its effect on health and wellbeing of individuals. As a result, the analysis and …
due to its effect on health and wellbeing of individuals. As a result, the analysis and …
An ensemble learning approach for estimating high spatiotemporal resolution of ground-level ozone in the contiguous United States
In this paper, we integrated multiple types of predictor variables and three types of machine
learners (neural network, random forest, and gradient boosting) into a geographically …
learners (neural network, random forest, and gradient boosting) into a geographically …
Forecasting air pollution particulate matter (PM2. 5) using machine learning regression models
From the past few decades, it has been observed that the urbanization and industrialization
are expanding in the developed nations and are confronting the overwhelming air …
are expanding in the developed nations and are confronting the overwhelming air …
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 …
Prediction, modelling, and forecasting of PM and AQI using hybrid machine learning
This paper proposes a combination of hybrid models like Input Variable Selection (IVS),
Machine Learning (ML), and regression method to predict, model, and forecast the daily …
Machine Learning (ML), and regression method to predict, model, and forecast the daily …
Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain
Air pollution, and especially atmospheric particulate matter (PM), has a profound impact on
human mortality and morbidity, environment, and ecological system. Accordingly, it is very …
human mortality and morbidity, environment, and ecological system. Accordingly, it is very …
Spatiotemporal causal convolutional network for forecasting hourly PM2. 5 concentrations in Beijing, China
Abstract Air pollution in Northeastern Asia is a serious environmental problem, especially in
China where PM 2.5 levels are quite high. Accurate PM 2.5 predictions are significant to …
China where PM 2.5 levels are quite high. Accurate PM 2.5 predictions are significant to …