English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In recent years, the harmfulness of air pollutants to human health and the environment have received widespread attention. Accurate prediction of air quality is essential to air quality management and policy development, however, the seasonal differences and the diurnal variations of air pollutants have not been fully investigated by traditional forecast models. Furthermore, the available spatio-temporal data for the problem have not been well processed to capture informative predictive patterns from past data. This paper proposes a Staging evolving spiking neural network model (Staging-eSNN), which uses a time series clustering algorithm to distinguish seasonal differences with diurnal variations of PM2.5 concentration. Based on this pretreatment, we use the Staging-eSNN model to predict PM2.5 concentration in the next 1, 3, 6, 12 and 24 hour in Beijing and Shanghai areas. Various evaluation indicators for prediction performance show that the Staging-eSNN model achieves appealing performance than SVR, RF and other eSNN models.

aerosol and air quality research

Evolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghai