Air pollution is strongly tied to climate change. Industrialization and fossil fuel combustion
are the main contributors leading to climate change, also being significant sources of air …

Abstract The Markov Weighted Fuzzy Time Series (MWFTS) is a method for making
predictions based on developing a fuzzy time series (FTS) algorithm. The MWTS has …

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 …

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 …

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 …

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 …

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 …

Severe deterioration of urban air quality in Asian cities is the cause of a large number of
deaths every year. A Markov chain–based IoT system is developed in this study to monitor …

The occurrences of extreme pollution events have serious effects on human health,
environmental ecosystems, and the national economy. To gain a better understanding of this …

Abstract Intensity–duration–frequency (IDF) curves can serve as useful tools in risk
assessment of extreme environmental events. Thus, this study proposes an IDF approach for …