[HTML][HTML] A new hybrid fuzzy time series model with an application to predict PM10 concentration
Fuzzy time series (FTS) forecasting models show a great performance in predicting time
series, such as air pollution time series. However, they have caused major issues by utilizing …
series, such as air pollution time series. However, they have caused major issues by utilizing …
Markov weighted fuzzy time-series model based on an optimum partition method for forecasting air pollution
Air pollution is one of the main environmental issues faced by most countries around the
world. Forecasting air pollution occurrences is an essential topic in air quality research due …
world. Forecasting air pollution occurrences is an essential topic in air quality research due …
[HTML][HTML] A spatial correlation prediction model of urban PM2. 5 concentration based on deconvolution and LSTM
Precise prediction of air pollutants can effectively reducre the occurrence of heavy pollution
incidents. With the current surge of massive data, deep learning appears to be a promising …
incidents. With the current surge of massive data, deep learning appears to be a promising …
Predicting daily air pollution index based on fuzzy time series markov chain model
Air pollution is a worldwide problem faced by most countries across the world. Prediction of
air pollution is crucial in air quality research since it is related to public health effects. The …
air pollution is crucial in air quality research since it is related to public health effects. The …
A novel stochastic fuzzy time series forecasting model based on a new partition method
Fuzzy Time Series (FTS) models are commonly used in time series forecasting, where they
do not require any statistical assumptions on time series data. FTS models can handle data …
do not require any statistical assumptions on time series data. FTS models can handle data …
Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model
An environmental problem which is of concern across the globe nowadays is air pollution.
The extent of air pollution is often studied based on data on the observed level of air …
The extent of air pollution is often studied based on data on the observed level of air …
A novel encoder-decoder model based on Autoformer for air quality index prediction
H Feng, X Zhang - Plos one, 2023 - journals.plos.org
Rapid economic development has led to increasingly serious air quality problems. Accurate
air quality prediction can provide technical support for air pollution prevention and treatment …
air quality prediction can provide technical support for air pollution prevention and treatment …
A comparative analysis of pre-and post-industrial spatiotemporal drought trends and patterns of Tibet Plateau using Sen slope estimator and steady-state probabilities …
Drought poses a significant risk to human life, agriculture, energy, ecosystem, wildlife, and
other aspects of the terrestrial system. Climate warming may increase drought hazards …
other aspects of the terrestrial system. Climate warming may increase drought hazards …
Time series-based PM2. 5 concentration prediction in Jing-Jin-Ji area using machine learning algorithm models
Globally all countries encounter air pollution problems along their development path. As a
significant indicator of air quality, PM 2.5 concentration has long been proven to be affecting …
significant indicator of air quality, PM 2.5 concentration has long been proven to be affecting …
[HTML][HTML] Evaluation of Machine Learning Models in Air Pollution Prediction for a Case Study of Macau as an Effort to Comply with UN Sustainable Development Goals
To comply with the United Nations Sustainable Development Goals (UN SDGs), in particular
with SDG 3, SDG 11, and SDG 13, a reliable air pollution prediction model must be …
with SDG 3, SDG 11, and SDG 13, a reliable air pollution prediction model must be …