With the development of the economy and society all over the world, most metropolitan cities
are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict …

A novel system regarding deterministic and interval predictions of pollutant concentration is
constructed in this study, which can not only obtain higher prediction accuracy in …

Air quality has always been one of the most important environmental concerns for the
general public and society. Using machine learning algorithms for Air Quality Index (AQI) …

Air pollution has lots of adverse effects on industrial production and public life. Thus, it is an
urgent task to construct an efficient air quality early-warning system to guide public life and …

In this paper, several extreme learning machine (ELM) models, including standard extreme
learning machine with sigmoid activation function (S-ELM), extreme learning machine with …

Suspended particulate matters (PM 10) is considered as a harmful air pollutant. Many
models attempt to predict numerical levels of PM 10 but a simple, clearly defined …

Air quality index (AQI) prediction is important to control air pollution. To improve its accuracy,
a new hybrid prediction model of AQI based on complete ensemble empirical mode …

Nowadays, with the burgeoning development of economy, CO 2 emissions increase rapidly
in China. It has become a common concern to seek effective methods to forecast CO 2 …

In order to improve the prediction accuracy and real-time of the air pollutant concentration
prediction, this paper proposes self-adaptive neuro-fuzzy weighted extreme learning …

The extreme learning machine (ELM), a single-hidden layer feedforward neural network
algorithm, was tested on nine environmental regression problems. The prediction accuracy …