Machine learning (ML) plays an important role in atmospheric environment prediction,
having been widely applied in atmospheric science with significant progress in algorithms …

Air quality models simulate the atmospheric environment systems and provide increased
domain knowledge and reliable forecasting. They provide early warnings to the population …

The paper presents the screening of various feedforward neural networks (FANN) and
wavelet–feedforward neural networks (WFANN) applied to time series of ground-level ozone …

Pore pressure (PP) is one of the essential and very critical parameters in the oil and gas
industry, especially in reservoir engineering, exploitation, and production. Forecasting this …

This study was undertaken to produce local, short-term, artificial intelligence-based models
that estimate the ozone level with special attention to the relationship between diurnal and …

In this paper, we are interested in the statistical modeling and forecasting of the daily
maximum ozone concentration in three monitoring stations from Tunisia. A large number of …

Air pollution and its consequences are negatively impacting on the world population and the
environment, which converts the monitoring and forecasting air quality techniques as …

In a competitive construction environment, contractors are often faced with a large number of
tenders that compel them to make the best decision in a limited time. In this paper, a …

Air pollution becomes more and more severe and can cause multifaceted harm to the
human body. Forecasting the air quality of a country is important to allow the government to …

This communication deals with the Abductive Network modeling approach to investigate the
phase holdup distributions of a liquid–solid circulating fluidized bed (LSCFB) system. The …